{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import all necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load gdp data\n",
    "\n",
    "gdp_df = pd.read_csv(\"GDP_PCT.csv\", header=0, parse_dates=['DATE'])\n",
    "gdp_df = gdp_df.set_index('DATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1947-04-01</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947-07-01</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947-10-01</th>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-01-01</th>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-04-01</th>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>290 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             GDP\n",
       "DATE            \n",
       "1947-04-01   4.7\n",
       "1947-07-01   6.0\n",
       "1947-10-01  17.3\n",
       "1948-01-01   9.6\n",
       "1948-04-01  10.7\n",
       "...          ...\n",
       "2018-07-01   4.8\n",
       "2018-10-01   2.9\n",
       "2019-01-01   3.9\n",
       "2019-04-01   4.7\n",
       "2019-07-01   3.8\n",
       "\n",
       "[290 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load nber recession dates data\n",
    "\n",
    "nber_df = pd.read_csv(\"USRECQ.csv\", header=0, parse_dates=['DATE'])\n",
    "nber_df = nber_df.set_index('DATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USRECQ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1854-10-01</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1855-01-01</th>\n",
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       "      <th>1855-04-01</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1855-07-01</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1855-10-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>660 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            USRECQ\n",
       "DATE              \n",
       "1854-10-01       1\n",
       "1855-01-01       0\n",
       "1855-04-01       0\n",
       "1855-07-01       0\n",
       "1855-10-01       0\n",
       "...            ...\n",
       "2018-07-01       0\n",
       "2018-10-01       0\n",
       "2019-01-01       0\n",
       "2019-04-01       0\n",
       "2019-07-01       0\n",
       "\n",
       "[660 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nber_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# limiting nber data to after 1947 since thats when gdp data starts\n",
    "\n",
    "nber_df = nber_df[(nber_df.index.year >= 1947)]\n",
    "nber_df = nber_df.iloc[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USRECQ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1947-04-01</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1947-07-01</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1947-10-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-01-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-04-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            USRECQ\n",
       "DATE              \n",
       "1947-04-01       0\n",
       "1947-07-01       0\n",
       "1947-10-01       0\n",
       "1948-01-01       0\n",
       "1948-04-01       0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nber_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine gdp and nber data into one single dataframe\n",
    "\n",
    "new_df = pd.DataFrame(\n",
    "    np.concatenate([gdp_df, nber_df], axis=1), \n",
    "    columns=['GDP_PCT', 'NBER'],\n",
    "    index=nber_df.index\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP_PCT</th>\n",
       "      <th>NBER</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1947-04-01</th>\n",
       "      <td>4.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947-07-01</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1947-10-01</th>\n",
       "      <td>17.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-01-01</th>\n",
       "      <td>9.6</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1948-04-01</th>\n",
       "      <td>10.7</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>3.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>4.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>290 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            GDP_PCT  NBER\n",
       "DATE                     \n",
       "1947-04-01      4.7   0.0\n",
       "1947-07-01      6.0   0.0\n",
       "1947-10-01     17.3   0.0\n",
       "1948-01-01      9.6   0.0\n",
       "1948-04-01     10.7   0.0\n",
       "...             ...   ...\n",
       "2018-07-01      4.8   0.0\n",
       "2018-10-01      2.9   0.0\n",
       "2019-01-01      3.9   0.0\n",
       "2019-04-01      4.7   0.0\n",
       "2019-07-01      3.8   0.0\n",
       "\n",
       "[290 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Miniconda3\\lib\\site-packages\\pandas\\core\\frame.py:4117: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# define recession data as whenever nber column equals 1 (which indicates a recession)\n",
    "rec_df = new_df.loc[new_df['NBER'] == 1]\n",
    "\n",
    "# define expansion data as whenever nber column equals 0 (which indicates an expansion)\n",
    "exp_df = new_df.loc[new_df['NBER'] == 0]\n",
    "\n",
    "# removing nber column as it is now unnecessary\n",
    "rec_df.drop(columns=['NBER'], inplace=True)\n",
    "exp_df.drop(columns=['NBER'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1949-01-01</th>\n",
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       "      <th>1949-04-01</th>\n",
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       "      <th>1949-07-01</th>\n",
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       "      <th>1949-10-01</th>\n",
       "      <td>-3.3</td>\n",
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       "    <tr>\n",
       "      <th>1953-07-01</th>\n",
       "      <td>-0.6</td>\n",
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       "    <tr>\n",
       "      <th>1953-10-01</th>\n",
       "      <td>-5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-01</th>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-04-01</th>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957-10-01</th>\n",
       "      <td>-3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-01-01</th>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-01</th>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>-3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-04-01</th>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-07-01</th>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-10-01</th>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01</th>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-04-01</th>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-07-01</th>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-10-01</th>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01</th>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-04-01</th>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-07-01</th>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-10-01</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01</th>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-01</th>\n",
       "      <td>7.2</td>\n",
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       "    <tr>\n",
       "      <th>1982-07-01</th>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-10-01</th>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-10-01</th>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-04-01</th>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-07-01</th>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10-01</th>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-01</th>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-10-01</th>\n",
       "      <td>-7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-01</th>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            GDP_PCT\n",
       "DATE               \n",
       "1949-01-01     -7.4\n",
       "1949-04-01     -5.2\n",
       "1949-07-01      2.3\n",
       "1949-10-01     -3.3\n",
       "1953-07-01     -0.6\n",
       "1953-10-01     -5.2\n",
       "1954-01-01     -0.6\n",
       "1954-04-01      0.8\n",
       "1957-10-01     -3.8\n",
       "1958-01-01     -6.0\n",
       "1958-04-01      3.9\n",
       "1960-07-01      3.4\n",
       "1960-10-01     -3.9\n",
       "1961-01-01      3.6\n",
       "1970-01-01      5.1\n",
       "1970-04-01      6.3\n",
       "1970-07-01      7.2\n",
       "1970-10-01      0.9\n",
       "1974-01-01      4.1\n",
       "1974-04-01     10.8\n",
       "1974-07-01      8.1\n",
       "1974-10-01     10.6\n",
       "1975-01-01      4.2\n",
       "1980-04-01      1.1\n",
       "1980-07-01      8.7\n",
       "1981-10-01      2.5\n",
       "1982-01-01     -0.8\n",
       "1982-04-01      7.2\n",
       "1982-07-01      4.2\n",
       "1982-10-01      4.4\n",
       "1990-10-01     -0.7\n",
       "1991-01-01      2.0\n",
       "2001-04-01      4.9\n",
       "2001-07-01     -0.1\n",
       "2001-10-01      2.4\n",
       "2008-01-01     -0.8\n",
       "2008-04-01      4.3\n",
       "2008-07-01      0.8\n",
       "2008-10-01     -7.2\n",
       "2009-01-01     -4.5\n",
       "2009-04-01     -1.2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP_PCT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1947-04-01</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947-07-01</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947-10-01</th>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-01-01</th>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-04-01</th>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            GDP_PCT\n",
       "DATE               \n",
       "1947-04-01      4.7\n",
       "1947-07-01      6.0\n",
       "1947-10-01     17.3\n",
       "1948-01-01      9.6\n",
       "1948-04-01     10.7\n",
       "...             ...\n",
       "2018-07-01      4.8\n",
       "2018-10-01      2.9\n",
       "2019-01-01      3.9\n",
       "2019-04-01      4.7\n",
       "2019-07-01      3.8\n",
       "\n",
       "[249 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define function for getting recession probabilities\n",
    "#function is based on the Dating Business Cycle Turning Points paper, Chauvet and Hamilton (2005)\n",
    "\n",
    "def get_probability_of_recession(gdp_growth):\n",
    "    \n",
    "    x_value = np.linspace(gdp_growth,gdp_growth, 1)\n",
    "    \n",
    "    rec_mu, rec_std = norm.fit(rec_df.values)\n",
    "    exp_mu, exp_std = norm.fit(exp_df.values)\n",
    "    \n",
    "    rec_p = norm.pdf(x_value, rec_mu, rec_std) * 0.2\n",
    "    exp_p = norm.pdf(x_value, exp_mu, exp_std) * 0.8\n",
    "    mix_p = rec_p + exp_p\n",
    "    \n",
    "    prob = (rec_p / mix_p)[0] * 100\n",
    "    \n",
    "    return prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the gdp data with predicted values for 2019 Q3 - 2021 Q2\n",
    "final_gdp_df = pd.read_csv(\"final_gdp_pct.csv\", header=0, parse_dates=['DATE'])\n",
    "final_gdp_df = final_gdp_df.set_index('DATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the gdp based recession index dataset\n",
    "rec_prob_df = pd.read_csv(\"REC_INDEX.csv\", header=0, parse_dates=['DATE'])\n",
    "rec_prob_df = rec_prob_df.set_index('DATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copying rec_prob_df to final_prob_df, which we will be adding to.\n",
    "final_prob_df = rec_prob_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REC_INDEX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1967-10-01</th>\n",
       "      <td>3.8348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-01-01</th>\n",
       "      <td>1.7614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-04-01</th>\n",
       "      <td>1.2127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-07-01</th>\n",
       "      <td>2.3009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-10-01</th>\n",
       "      <td>6.3338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>1.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>1.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>2.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>2.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>3.9000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            REC_INDEX\n",
       "DATE                 \n",
       "1967-10-01     3.8348\n",
       "1968-01-01     1.7614\n",
       "1968-04-01     1.2127\n",
       "1968-07-01     2.3009\n",
       "1968-10-01     6.3338\n",
       "...               ...\n",
       "2018-04-01     1.1000\n",
       "2018-07-01     1.5000\n",
       "2018-10-01     2.4000\n",
       "2019-01-01     2.9000\n",
       "2019-04-01     3.9000\n",
       "\n",
       "[207 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_prob_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>3.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>4.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>-18.488588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>17.337566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>-9.270814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>17.026945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>-9.446802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>1.917489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>1.706170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  GDP\n",
       "DATE                 \n",
       "2019-01-01   3.900000\n",
       "2019-04-01   4.700000\n",
       "2019-07-01   3.800000\n",
       "2019-10-01 -18.488588\n",
       "2020-01-01  17.337566\n",
       "2020-04-01  -9.270814\n",
       "2020-07-01  17.026945\n",
       "2020-10-01  -9.446802\n",
       "2021-01-01   1.917489\n",
       "2021-04-01   1.706170"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get last 10 values of final_gdp_df\n",
    "final_gdp_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#last 8 values of final_gdp_df are the  values for 2019 Q3 to 2021 Q2\n",
    "\n",
    "#for loop converts the last 8 gdp values to recession probabilities using the get_probability_of_recession function\n",
    "# ... and adds those values to the recession index dataset\n",
    "for i in range(0, 8):\n",
    "    probability = get_probability_of_recession(gdp_growth=final_gdp_df[-8:].values[i])[0]\n",
    "    final_prob_df = final_prob_df.append({'REC_INDEX': probability}, ignore_index=True)\n",
    "    \n",
    "final_prob_df.index = pd.to_datetime(rec_prob_df.index.append(pd.Index([\"2019-07-01\", \"2019-10-01\", \"2020-01-01\", \"2020-04-01\", \"2020-07-01\", \"2020-10-01\", \"2021-01-01\", \"2021-04-01\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REC_INDEX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>3.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>21.990390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>99.984529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>1.293377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>97.954869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>1.351767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>98.115993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>34.114781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>35.728575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            REC_INDEX\n",
       "2019-01-01   2.900000\n",
       "2019-04-01   3.900000\n",
       "2019-07-01  21.990390\n",
       "2019-10-01  99.984529\n",
       "2020-01-01   1.293377\n",
       "2020-04-01  97.954869\n",
       "2020-07-01   1.351767\n",
       "2020-10-01  98.115993\n",
       "2021-01-01  34.114781\n",
       "2021-04-01  35.728575"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get last 10 values of final_prob_df\n",
    "final_prob_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seems that between 2019 Q4 and 2020 Q4, \n",
    "# recession probabilities are unstable and has outliers due to unstable economy.\n",
    "# After 2020 Q4, it seems that the recession ends since: \n",
    "# the recession probabilities are under 67% for two consecutive quarters.\n",
    "# Therefore, disregarding those values (2021 Q1 and Q2)...\n",
    "# and interpolating the data for quarters (2019 Q4 to 2020 Q4) seems like the best option to \n",
    "# get rid of the outliers and make data more smooth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deleting (2019 Q4 to 2020 Q3) so that we can interpolate\n",
    "final_prob_df.iloc[-7:-3,] = float('nan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REC_INDEX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>3.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>21.990390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>98.115993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>34.114781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>35.728575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            REC_INDEX\n",
       "2019-01-01   2.900000\n",
       "2019-04-01   3.900000\n",
       "2019-07-01  21.990390\n",
       "2019-10-01        NaN\n",
       "2020-01-01        NaN\n",
       "2020-04-01        NaN\n",
       "2020-07-01        NaN\n",
       "2020-10-01  98.115993\n",
       "2021-01-01  34.114781\n",
       "2021-04-01  35.728575"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2019 Q4 to 2020 Q3) is now empty\n",
    "final_prob_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#interpolate through those nan values\n",
    "final_prob_df = final_prob_df.interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REC_INDEX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>3.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>21.990390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>37.215511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>52.440631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>67.665752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>82.890872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>98.115993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>34.114781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>35.728575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            REC_INDEX\n",
       "2019-01-01   2.900000\n",
       "2019-04-01   3.900000\n",
       "2019-07-01  21.990390\n",
       "2019-10-01  37.215511\n",
       "2020-01-01  52.440631\n",
       "2020-04-01  67.665752\n",
       "2020-07-01  82.890872\n",
       "2020-10-01  98.115993\n",
       "2021-01-01  34.114781\n",
       "2021-04-01  35.728575"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#see final interpolated values\n",
    "final_prob_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing index name to DATE\n",
    "final_prob_df.index.name = \"DATE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REC_INDEX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>3.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>21.990390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>37.215511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>52.440631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>67.665752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>82.890872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>98.115993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>34.114781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>35.728575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            REC_INDEX\n",
       "DATE                 \n",
       "2019-01-01   2.900000\n",
       "2019-04-01   3.900000\n",
       "2019-07-01  21.990390\n",
       "2019-10-01  37.215511\n",
       "2020-01-01  52.440631\n",
       "2020-04-01  67.665752\n",
       "2020-07-01  82.890872\n",
       "2020-10-01  98.115993\n",
       "2021-01-01  34.114781\n",
       "2021-04-01  35.728575"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_prob_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save final_prob_df data as final_rec_prob.csv file\n",
    "final_prob_df.to_csv('final_rec_prob.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8T7HeFVDWs6NCOHFbWhpjxmO9iMAa1ALtGAXwrIjkBinrg/WEguDxap7DLkeE0GMwIUj+Y1jDQCw2QHUkeGIKdXF72AXgYQkcR2UK1nsPYEiEfVc5xpgO2OVI+7HeTsF42/ns5Tcunh3gQnnzHW54rnErY8wV/oXG7sLnDsac6ifi8RDsjI1HNR1rpEnHeipejjVOHQ/MctoLh75YTz0ojVkUKW6vrMwI6u0K0oaHl7Ger38XV9DvKsRznTZSGk/JH88OdPWI0CsnGM4ze6rTZj6lnp1uari+h/JIc5fVCCrl238Sdn4lYj1x7wtd45DgmQ+RzCconVNhewoaY4ZR+nf+UREJGG9PRLqKiBGRlhHqpCiKUiHUIKUoinLoqOdK7hent7EeHlv8Kxgb+Ptk5/AffkYtnwS0cOSaupo42/n8MZBRJgSepTIXldPn/wvQ5/fYf6gbAj8YY/5qjGldjtFhLXZ5RLxT505jTHtjTKilPqFoifXgAuthEwzPjkp1jTGNg8j8EChTbEBmz4vDwRjRvAGGsQau34C/Y41zGcAACb4d/X9DtOsJcP2HiKwPJCA2QO88P3l//hCRP4LU3wP8HKy+Maa+MeYhY8z3TmDkIte5/uKIpVB6rfzZT5BzFBts2xOIutKDeR8CPPdUNPBbiHvKs/W8e8kmrvypxpgnjDHnRmDwrS5eAzY436c4S+5qG2PineWLX2GfEwccGX/DY5Tr8xdgiIhsBBCRAyIyHespB/YZdG2YenmCmW8BPo/gfIIRrqEfSpecgX3elRYYMxy7hHoppcHeqxrPvfRNsB8wRORX7JJlt/zB8jylMcNuFJFQS/IqFWNMLHa3xNOwz5yhIrK9qvoPQCTzCUrnVMBNHsoIG9OdUkPsV5T++KEoilLtqEFKURTlEOH8ymiwz9rjsXFC9mB3ArolSLUGlP6zWQtfo5Z/inXk3Mau+s7nxgjV9Wy3nVJOn55d9rx9ishu7LK9XVjPoclY763dxpiPjTHDnN2zcNUpwnq5bMRuE/84sAzYa+yugjdE+PLt3j2vjKHPxeYgddzsC1Hf4yEVG0KmPHKwL3c7sB4/a4EvsEuZ2ojIohB1QxkZPecT6vyhdAyCnX959T3lPvWNMV2x1/0BbMyjWljPhQzsubo9RYLt1pghzlblkfR9mOK5p6IJfU/VcdVx38tjsAa4Gq7v+4wxi40xDxpjjucww/HovBB7X6dgvX92YZcWL8QazCdRGjsvy68J9733VCBPOccotcE5LHfnRidujmf3vzel4rsIur1Y6gSVKovbi8V7vsaY+sBT2GfKyIPQ62CprOdG2BhjJmH/HgKMFpG3g4i650Mobzh3Wajnt2cnx2nARdixHyIiX4fW+JDhmVORzCconVP+908ZnOfyJ9jYc//BBo6vrrmmKIpSBjVIKYqiHGLEsk1EXqY0JsZjxphASx/cHkIdPUatclKgANOR/uLq6feOMPs83+8c52ANS1djPcDWYZePDcBuy/2TfyBxsVu6n4hdavcKdtvrJEqDbq82xpxM5IR77pGOUWXhDjB8vIi0FpG+IvKkiJT3ghHOi8TBnn/E4+IEfH4X6/n0E9bro4aIpIpIPbEBlru6q1RW34cxnntqeZj3lBERr8FURLJEpDt2U4B/Yg06xVgPlXFYr6vLq/aUykdEVgCnYI1o32CNT+uwcYj6YYOxN3HE1/pVdxtFQgVs95Q1DSHj4WpK4/68HoZ8MNZjl5YB/F8E9dzB6te4vv8T6xH3IrDOGJPiTriM3q78uArqHg5V8tw0xjyFjZMIdue8ySHEt7q+Nwwq5Vu2LZiQY4x6FxtzqgjrGTUztMaHFM/y+FbONS8Xx/O4vXO4phzZrliPwGRsbKj+En4wfkVRlCpBDVKKoihViIjMx8YHMsDkAEvUdri+VyROkeef8WYR1vMsV6hwbCQRyRGRt0XkahFpBTQG7sXGHDqVAEtSRGS/iMwQkRtE5BTsr+83Yz3JmhA8pok/bs+hYEvxABq5voe9g98RgmcMQp0/lI5BsPNvFCTfg+flzz3mXZx+i7AB778MEP+sPuVTz1lOE0nfhyuee6rlwSy1E5FvReQuEemCNfJeTKnx9k1jTKTeFYcc51nwlIj0FJETRKSViFwiIp9jN2RIcET9l2f+4m4mRBcmDBkPnmV93wRbyhoOzpJdj759jDHBvPz8ccdaW+D63tz5vAXr1eOf7nLKo115ZXZVrQQq67lRLsaYp7EbK4CNcTgplDzWYOO5xqeEkPOUbQ623NnlGTUY+5waJiIzwlL80OHxzIrG3tfh0IPSWIHzgwm5jFEpWGNU3wDPZEVRlGpHDVKKoihVz0NYT4eTsL/eexGRnZR6DVQkcPNC5/MMZ6lKuHhetC6K4EUrJCKyWUQexS7RAev5VF6dXSLyItaQBdApzG2811EaBLpnCDmPZ9cOEdkURrtHEkucz2bGmBaBBJyXsu7O4eIg7TQ3xjQLUj+NUo+PJa4iz8vsdvHdTt7N+UHy3cRRGgfNv+8orLeQf9+HCs9ysVCx0ELhuacSgIEHrw6ISL6IfAxc5mQlYo2BRxKewMq/isjPfmVzXN/bhmijjfMZMNaZB2PMOUBr57CiwczdvOB8puEbnD1Y/20o3WxhviceVhUSzhz23EvnBYv7Z4w5Bbu8FII/N0LiLNPzjNnfRCRUoH8ARGQfNkYhWK/LQO1GAb2dwzlBZGKwMaMuo9QYNT187Q8ZH1JqELy3PA845/p4AswXY72PA8mdgxqjFEU5QlCDlKIoShUjIr8D7zuHDwTwCHnF+exjjLmMEBhj/INrv4+NURQHPBmBWp7d52oBj5bTZ5zbaGWMKS+wqmeZi3e5WQR1oGzg4zI4sWY+cA5vNsaUiXNijGlE6S6H08pr8wjkC0p3L3wwiMzNlL5YhhqDsUHy78QG0j0AuJe6eIyBxwfy2DHGNAH+GqI/N/cHeTG+ltK4TO8HKK9ssp3PmiGlgvM98Kvz/R/leTK572VjTIzzoh0M9/1xxMSDMcacj42hB9Yw74PjwfQf5/BvgcbAGDOYUg/Q2eV06Qlmvhv4d6T6BuBj4Fvn+9+NMX2DCTrX+wPs/VKC3z0lpbuZBUzAREe02JV/R4T6hjOH33M+mwLXBJHxBMHeQemmCGHjGKM8y/RuC8cY5eIt57OXsTtX+jOE0qWbZWJRuYxR7mV6h4MxCrG7ud7vHLYFXinnvp8IdHO+v+b8L+GD4xn1GdYY9S1qjFIU5XBHRDRp0qRJUyUlbGwXsY/XkHKnYF9SPDsMucsSgB+dsgPYl4FGrvJkrJfLC8CuAG3/1aMD9iWsvausFjaY62wgya/es6567/vVi8HuSPR3YBPQ2VX2EPYf4CuBhq78eOzLwl6nzbddZSOw/yxfD5zg188F2FgyAvzHT8fznfyiAOfdFGuQ8ezodpar7BxswG3BLjlp4Fe3pevcG/m37ZLb7Mj8JcJ5EeNqf2yEdcPSzZG9zSX7PFDXNWduc+aTAFMD1H3YKfOM4ZNAbacsFfvLvGfOPuFXtxaQ65TNA1o5+dHYnbTWY4NbBzwPrNeMYI2pB7C//Dd03Q83YgNjCzAjgO6e+usqsewCp2y/e74HkJvqyE0JUHYWdsmqYL34BgGJrvKG2J0rvwZe9Lvm67Bb0bcHYlxl7bFLvwRrcKgZ5DqWO1+CnE8sNsiyJ13qau9Uv7KYAPVvx973dV15DbBej3lOO2+F6L+jM+ae51ATl16XYY1Lgl22GBeinVTXnHwm0nEI0e7xWM8sz/P5aaC1q7wm1rCziQre837XscyzLoI2HnXaWA0cH0LuI0euAGu0TnRdt9dc5zGyAjo86ao/uoLzcYVTfxNwnpMfBVzh3AMCzApQNxprFPTcx5dWcBzdc76e63xu9CtLrGD7r7jaXIgN1h/rOs+uwKcumZ+BlADtnI1d2inYZ0RyBXT5jiDPRE2aNGk6FKnaFdCkSZOmoykRpkHKkfW8BGwC4v3KjsO+2Isr7cXuqlPiyssP0vZYP7k81z/unpTiVycGG+dJ/OplYn9Zduef6ar3sF9ZrlPH3f+v+L6gjvSrU4A1WBS78jYBJ/rpGNQg5ZT3oNQA5jFw5LiOM4GzA9Q7WgxSBl/DYgn2Bf6AK++rIC8znus4l9KXyGKnvvv6f+E/X536o/yuaTalRqQMbID7gOfhmg/rsJ4Unrnjr/tPQHqAvg+FQSoW+M3V927s7m4bsDtVeeSCGqSc8j7OvPO0U+TM9Tx8x8vfIOUuO+DUKXTlFQCXhLiO5c6XIPqe79d3qNQ1QP1PXOWBnjsvA9Hl6DDENXc8Y+8+XoPLkB2kjRtc8qdGOg7ltF0Pe5+4zyufUmOuJ+3HLk+rSB+VYZBq4xq3YmxcM88cru+Sq4n9gcA933bj+wx/tAL9n+Cq7+k/VLotRDsbXW3l+M2HJUBagHo9/K5Fef0PCtCG+9ldXorY8Ojq535nDrnHKxPf55/n+Vs7SBv/8btnQp3roiBtqEFKkyZNVZp0yZ6iKEr14VmS0Qj78uRFbCypHthd+T7EGkLisTFjNmM9kv4KBIwVJHbnvdOAKYDbrX8NdpehS7CGI3edIhG5FeiAXcK3FvtCkor95/Y77FKw9iLyg6vqi47+72N/yS5w1VkAjAY6iYg7EPVMbPysN7HeTNnYuCz7gB+wBrVTRMR/F66QiMg32NhcT2G9AqKwRpqV2F2tThKRhcFbOLIRy2hsvK6Z2CU2KdjxnQcMB/pIOUs4RGQMMAz7a30U9mXuZ2wA5v4iUhigzmSs0WkB9oUxFmtUfAZoR+mOUuWdwzNYr6o52PlXjN1V7QGsMbHcrc4rAxE5AJyH3ZltA9bLrKmTwtoRy2nnS6yB6T5sXKm9WANAEfZ+mYL1WnTHJNqIjTs1CXs/7ABqOHVWApOx90egHcI8gd834LtJQlXxInbjhjXYl+lYrEfRW1gD1g1SzrbzIvIe1hPsVaduEnYO/gjcDXSQ8gOUe5br/Sgiv4aUjBAR2SF2p9FeWA+itdhzdce72wycLCJPVWbfkSAiq7F/R2ZjDZq1KZ3DMS65Pdi5fh32/t2HnePbgRlANxG5pwIqRPl9r1dOCnhfOdf6VGAC9p4x2PmwBLub41kSOJi5u//YMPqv8OYDB4uITARaYT2iF2GNUWm4rhNwE3YJXmaQZtznm07ocz2uMvVXFEWpKEZEqlsHRVEURVEUpRIwxmzAGhyGi8hb5YgrlYwx5kpKd1IdJyLjy6miKEExxjTAGqiaAsuwSxb3hK6lKIpy5KAGKUVRFEVRlKMAY8wJWI/IVVgPqnI3BFAqH2PMHViPTIAx1eklpRz5GGNaY70ra2ONU71EJDd0LUVRlCMDXbKnKIqiKIpydHCe8/mAGqOqDxF5AhvsHOBJY8z11amPcmQjImuwy3rzsRslzDLGJFSvVoqiKJWDekgpiqIoiqIoiqIoiqIoVYp6SCmKoiiKoiiKoiiKoihVSkz5Ikc/xpifsbtNrKtuXRRFURRFURRFURRFUY4SWgI7ReR0/wI1SFmOS0tLa3jaaac1LF9UURRFURRFURRFURRFKY9ly5axd+/egGVqkLKsO+200xrOnz+/uvVQFEVRFEVRFEVRFEU5KujevTsLFiwIuBpNY0gpiqIoiqIoiqIoiqIoVYoapBRFURRFURRFURRFUZQqRQ1SiqIoiqIoiqIoiqIoSpWiBilFURRFURRFURRFURSlSlGDlKIoiqIoiqIoiqIoilKl6C57YZKdnU1GRgYHDhyoblUURTnGiY2NpW7duqSmpla3KoqiKIqiKIqiKBVCDVJhkJ2dzY4dO2jYsCGJiYkYY6pbJUVRjlFEhPz8fLZs2QKgRilFURRFURRFUY5IdMleGGRkZNCwYUOSkpLUGKUoSrVijCEpKYmGDRuSkZFR3eooiqIoiqIoiqJUCDVIhcGBAwdITEysbjUURVG8JCYm6hJiRVEURVEURVGOWNQgFSbqGaUoyuGEPpMURVEURVEURTmSUYOUoiiKoiiKoiiKoiiKUqWoQUpRFEVRFEVRFEVRFEWpUtQgdYwhIjRv3hxjDOvWrYu4fkZGBuPGjWPDhg2Vr5zDuAnb3W4AACAASURBVHHjqFOnTliyF110EePHj/ceL1myhOHDh9O6dWuioqIYPnx4wHqbNm1i0KBBpKamkpaWxpAhQwIGiM7Ly+Puu++mSZMmJCQkcMIJJ/D44497y998802MMQHTDTfcUK7+mZmZ3H777TRr1oz4+HgaNmzIyJEj2bx5c8h6t912G8YY7rjjjnL7ePzxx5k/f365cuGQnZ3Ngw8+yBlnnEFaWhr169fnkksuYe3atWVk9+7dyzXXXEN6ejppaWlceeWVZGZm+sh89dVXDB06lGbNmmGMYdy4cQH7XbFiBb179yYpKYk6depw0003kZOTE5bOf/75JyNGjKBhw4bEx8fTrFkz/va3v5GVleUjt337dgYOHOi91g0aNGDw4MH89ttvPnL9+/dnwoQJYfWtKIqiKIqiKIqiBEYNUscYixYt8hqT3nvvvYjrZ2RkMH78+ENqkAqXH374gXnz5nHLLbd48/773//y3Xff0alTJ+rXrx+wXlFREX379mXVqlW88cYbTJkyhSVLltC3b1+Ki4u9csXFxfTr14+PP/6YiRMn8sUXX3D//ff7tNW/f38WLVrkkyZNmgRA3759Q+q/detWzjjjDGbMmMHdd9/NnDlz+Mc//sH3339Phw4dWLNmTcB6K1eu5PXXXyc1NTWscapMg9Sff/7Jq6++Sp8+fZgxYwYvv/wy27Zt48wzz2TTpk0+sldccQXz589nypQpvPnmmyxevJiLL77YR+aLL77gl19+oWfPniQlJQXsc+/evfTo0YP8/Hzef/99nnjiCT788EP+8pe/lKvvihUr6NChA99//z0TJ05kzpw53HvvvcyYMYPOnTuzY8cOr2xeXh7p6elMmDCBL774gqeffpo1a9bQo0cP9uzZ45W75557eOqpp3zyFEVRFEVRFEVRlAgRkWM+AfO7desmwVi5cmXQsiONUaNGSXJyspx55pnStm3biOv/+uuvAsi8efMqXzmHBx98UGrXrl2u3LBhw2TYsGE+ecXFxd7vHTp0kKuvvrpMvXfffVeioqJk7dq13rz//e9/Asj06dO9eS+++KLUrFlTduzYEZH+N910k6SlpUlBQUFIuYEDB0qtWrVk8+bNPvl79+6VVq1aSadOnQLW69mzp4wdO1aaNm0qY8aMKVef2rVry4MPPhi2/qHIycmRvLw8n7zMzExJTk6WcePGefMWLlwogCxYsMCb98MPPwggX331lTfPfb2C6fnII49IjRo1JCsry5v38ccfCyCLFy8OqmtJSYmcdtpp0qpVK9m7d69P2ebNm6VWrVoyePDgkOe7du1aAeTDDz/0yW/RooU8++yzIetWBUfTs0lRFEVRFEVRlKOPbt26CTBfAthi1EPqGKK4uJjp06czYMAArr32WlauXMkvv/xSRm7jxo0MHTqUOnXqkJSURLt27Xj33XfZsGEDp556KgDnnXeed2kalC5d819G1axZM59lZZ9++im9evWibt26pKam0rlzZ+bMmRPxuezbt4+ZM2dy2WWX+eRHRZU/pZctW0bTpk1p1aqVN69du3bUq1ePTz/91Jv3+uuvc/nll1O3bt2w9SouLubDDz/k0ksvJT4+Pqjchg0bmDVrFrfeeisNGzb0KUtNTeX+++9n8eLFLFy40KdsxowZrFq1invuuScsfZo1a0ZmZibjx4/3Xi+Pt1ReXh6jR4+mfv36JCQk0KlTp3KvRXJyMomJiT55tWrVomnTpj5LHj///HPq1avHueee680744wzaN68OZ9//rk3L9zr1bFjR2rWrOnN6927N8YYn+vlz3/+8x+WLVvG2LFjy3iTNWzYkNGjR/Phhx+GXB5Zu3ZtAPbv3++TP2jQIN5+++1ydVcURVEURVEURVECowapY4hvvvmGHTt2MGTIEC677DJiY2OZNm2aj0xGRgZnnXUWixcv5oknnmD27NmMGDGCTZs20aBBA/71r38B8Pzzz3uXqEXCH3/8wUUXXcQ777zDhx9+yNlnn03fvn3573//G1E7CxcuJD8/n7PPPjuiegAFBQXExcWVyY+Pj2fVqlWANUD8/PPPNGrUiCuvvJLExETS0tK45ppryM7ODtr2119/TUZGBkOHDg2pw7fffouIlFnC5sGTv2DBAm9efn4+Y8aM4dFHHyU5Obnc8wSYOXMmaWlpjBgxwnu9/u///g+A6667jjfeeIP777+fmTNn0rhxY/r37893330XVtsedu7cybp162jbtq03b/Xq1bRp06aM7EknncTq1asjaj/Q9YqJiSEqKsp7vQLxn//8B4CBAwcGLL/44ospKSnh22+/9ckvKSnhwIEDbNy4kVtvvZWmTZvSv39/H5mzzz6bpUuXlolDpSiKoiiKoiiKooRHTHUrYIxpCdwJdAZOAb4Vke5+Mga4F7gJqAMsBkaLyDI/ubbAc8BZwB5gCjBeRIqpZMbPXsHKrcENE4eStsen8uBFJ0dcb9q0adSsWZMLLriAuLg4evXqxXvvvccjjzzi9XR6+umn2bt3L0uXLqVBgwYA9OzZ09tGu3btrA5t29K5c+eIdRg1apT3e0lJCeeddx4rVqzgtddeo0uXLmG3s3TpUurUqUO9evUi1qFly5a89NJLZGZmej1gtm7dypYtW7xeTZmZmRQVFfH444/Ts2dPZs2axZ9//smdd95Jbm4uH3zwQcC233vvPerWrUuPHj1C6rBlyxYAmjZtGrA8LS2NtLQ0H++df/zjHzRo0CCs2EkeTj/9dGJiYmjUqJHP9Vq1ahXTpk3jjTfe4OqrrwagT58+tGvXjgkTJvDll1+G3ceYMWNISUlhyJAh3rysrCwfjyYP6enprF+/Puy2wV6vd999lwMHDhAbGwvY619cXMzu3buD1tuyZQs1a9YkLS0tYLln7P09pG6++WZefvllAE444QS++uoratSo4SPTvn17RIQlS5bQq1eviM5HURRFURRFURRFOTw8pE4G+gFrnRSIe4AHgMeAi4AcYK4xxhu12hiTDswFBBgIPASMAcaXae0YpLCwkJkzZ3LJJZd4vU2GDh3Khg0b+P77771y33zzDRdccIHXGFXZbN68mauvvpqGDRsSExNDbGwsc+bMCbhLWyi2b98e9k58/gwbNoyEhARGjBjBn3/+yYYNG7jmmmsAiI6OBqyxDKwBZfr06fTq1YsRI0YwadIkpk+fzu+//16m3f379zNz5kwGDx7sbedg8RgK//jjD5544gkmTZrkzTsYFi9ejIgwePBgb15UVBSDBw+OyEPqxRdfZOrUqUyZMsVr3PPX3Y2IRKz/ddddx86dO7nlllvYvn07K1as4OabbyY6Ovqgxtmjh78+9913Hz/++CPTp0/nuOOOo3fv3j7BzwHv3Nu+fXuF+1cURVEURVEURTmWqXYPKWC2iHwMYIyZgfWA8mKMScAapP4hIpOdvEXABmAUMNYRvRFIBC4VkWzgK2NMKjDOGPO4k1dpVMRDqTr5/PPP2bNnD/369fPuDta9e3fi4+OZNm0aZ511FmA9gzp16nRIdCgpKWHAgAHs27ePhx56iJYtW5KcnMzf//53n/hD4VBQUBAyRlMoateuzbvvvsuIESO8XjIXX3wx/fr18y7HS09PB6BLly5erxzA6/m0cuVKWrRo4dOuZ4zLW64HeONGbdy40et15iY7O5u9e/d6DYP33HMPffv2pU2bNt7rV1JSQmFhIXv27CEtLS0iQ8+2bdtISUkps7NdvXr1yMvLo7CwsNzxnTVrFrfccguPPfYYl1xyiU9Zeno6O3fuLFNnz549AT2nQtGmTRteeeUVbr/9dl5++WWioqK4/vrrMcaE9JBr2LAhe/bsITs7O+COhJ6dIv2Nr02aNKFJkyZ06tSJ3r1707RpU55//nkeeughr4xnbAoKCiI6F0VRFEVRFEVRFMVS7R5SIlJSjsjZQCrgXSMlIrnAbKCvS64v8KWf4ek9rJGqW+Voe+TiiRU1ePBg0tPTSU9Pp3HjxhQWFvLBBx9QXGxXNdauXZtt27ZF3H5CQgJQNvizO8bOunXr+Pnnn3nuuecYMWIE3bp1o2PHjuTn50fcX61atbyGmYrQv39/Nm/ezIoVK9i0aRMzZ85k/fr13mVtSUlJAZfTid2VMWAw7vfee48mTZqEFdfqnHPOwRjDrFmzApZ//PHHAN621qxZw7///W/vtUtPT2fTpk1MnjyZ9PR07xLAcGnQoAE5OTnk5eX55O/YsYOkpKRyjVELFy5kyJAh3Hjjjdx5551lytu0aRMwVlSw2FLlce2117Jjxw5++eUXtm7dyuTJk1m3bl3IZaPdutnbPtgYe/I9xthApKam0qJFizLLDD1zr1atWhGdh6IoiqIoiqIoimKpdoNUGLQBioHf/PJXOWVuOZ83YBH5E8jzkzvmyMnJ4ZNPPmHo0KHMmzfPJz311FPs2LGDefPmATZe1JdffllmiZIHz3I/f8+QRo0aAfgEmf7hhx98AoB7DE9uY8fGjRsjDmgO0Lp1a7Zu3UphYWHEdT3ExMTQtm1bGjVqxIIFC1i9ejXDhw/3ll944YV89913Pka2r7/+mqioKO9ugx7y8vKYPXs2Q4YMCctTqVmzZgwYMIBJkyaVMQDm5OTw8MMPc8IJJ3iNKlOmTClz7erVq8fll1/OvHnzOO644wL2U1wixMbFkZ2bR05BETkFB8gpOMD/deiAMYYZM2Z4ZUWEGTNm0LVr15C6r1ixggsvvJALLriAZ599NqBM37592b59u8/yvyVLlrB+/Xr69u0bsE55JCQkcOqpp1KvXj2mTp1KSUkJl19+eVD5c845h9NOO40JEyawb98+n7Jt27bxzDPP0K1bN0444YSgbezatYs1a9bQvHlzn3yPd9WJJ55YoXNRFEVRFEVRFEU51jkcluyVRzqQEyAweRaQZIyJE5H9jlwgl5ksp6wMxpjrgeuB1hXxCjpS+Pjjj8nLy+PWW2/lzDPP9Cnr0qULEydOZNq0aZx//vncfvvtvP3225xzzjncf//9NG7cmFWrVpGbm8tdd91FkyZNSExM5K233iItLY3Y2Fg6duzIGWecQcOGDRk9ejQTJkxg9+7dPP744z5Lpdq0aUOjRo0YM2aM10jw4IMPepevRUKXLl04cOAAv/76Kx07dvTm79y507szXVZWFhs3bvQaXS677DKv3J133kmXLl1ISUnhxx9/ZOLEiYwdO9bHe+fOO+9k6tSpDBo0iJtvvplNmzZx9913c+2119KkSRMffWbNmkVubm5Yy/U8vPDCC5xzzjl07tyZe++9l7Zt27Jx40Yef/xxNmzYwLx587yeWO5z9JCQkEDjxo3p3r17wPb3FRxgc1Y+jZu3ZPbsTzj5jHNJSkqmWYuWNGrQnKFDhzJq1Ciys7Np2bIlr776KqtXr+bFF18MqnNGRgYXXHABKSkpjB49mh9//NFblpqa6t1p76yzzqJPnz5cddVVPPHEE0RFRXH33XfTtWtXzj//fG+djRs3snjxYsB6161cuZIZM2aQnJzsNVxlZ2czceJEzj33XGJiYpg3bx5PPvkkr776akgPJWMM77zzDueddx5nnXUWd911F82aNWP16tVMnDiRoqIiXnvtNa/8k08+yR9//MG5555L3bp1+eOPP3j66aeJj4/nhhtu8Gl7yZIlpKWlcfLJB7d0t6REyMwtpE5KfKXEBlMURVEURVEURTliEJHDJgEzgPl+efcDWQFkr8MGMI91jg8AtwaQ2wJMLKff+d26dZNgrFy5MmjZkUD//v2lVatWQctvuukmqVmzphQUFIiIyIYNG+Tyyy+XmjVrSmJiorRr106mTZvmlZ86daq0atVKYmNjxU4hy48//igdO3aUxMREOe200+S7776Tpk2bypgxY3xkOnXqJAkJCdKyZUt544035Oqrr5YOHTp4ZR588EGpXbt2ued1yimnyEMPPeSTN2/ePHHmRZnkZvDgwXLcccdJXFycnHLKKfLKK68E7GPx4sXStWtXSUhIkLp168qtt94q+fn5ZeQGDhworVu3Lldnf3bt2iW33XabNG3aVKKjowWQunXryrJly3zk9ubvl8ycQikuKfHm+Y+th6LiYtmUmSv/25Qla7Zly/zvvpeOnc6QpKQkAeStDz+RDbtyJDc3V0aNGiV169aVuLg46dChg3zxxRch9Q01vv73UFZWlgwfPlzS0tKkRo0aMnToUNm5c6ePzBtvvBGwraZNm3plcnJypFevXpKeni4JCQnSsWNHmTlzZpgjLLJx40a59tprpUGDBhIVFSWAtG7dWv744w8fua+++krOO+88qVOnjsTHx0uLFi1kxIgRsnHjxjJtDhgwQIYPHx62DsHYm7df/rcpS3IKDlSo/pH+bFIURVEUpeooPFAsv2zaI+8s2iB3fLBMLnn+O/n5z6zqVktRlKOcbt26ib+dx5OMODFxDgc8Qc1FpLsr72bgWSBeXF5Sxpg7gXEikuwcZwDPi8h4vzZzgPEi8s8Q/c7v1q1bt/nz5wcsX7VqFSeddFKFz0s5NDz99NO89tprLF++vLpVqTRee+01Ro4cydSpU7nyyiu9+b/t2Ef+gWJio6OoWyOe9OQ4ovw8akSEnMIiNmflU1RcQp0a8dSrkUBUlK/cbzv2ERsdRbM6yVVyTocbY8eO5fHHH2fOnDlBvctCsXfvXurVq8fcuXPLXd5YHnvy9vPn7jxaHJdCcnzkDqv6bFIURVEUJRwemr2SqT9sZH+Rs5N0UixZeQe4t28bbujWopzaiqIoFad79+4sWLBggdvO4+FIiCG1GogGWvrl+8eMWo1frChjTGMg2U9OOUq4/vrr2blzJ3Pnzq1uVSqNESNGMHr0aEaMGMG3337rzS8RSIyNJjY6ii178lmzfR+79hWSmVvI1j35/L4zh1XbsvljVy5RxtDiuBQapCWWMUYBGGPdkI5VJkyYQP/+/bn00ktZs2ZNxPVffPFFOnfufNDGKLAxvqA0WL6iKIqiKMqh4MsV2zmxXgqTh53Ot3edx08P9CI6ypBdcKC6VVMU5RjmSIghtRDIBgYDDwMYY5KAi4BXXHKfA3caY2qIiCeC8RVAPrCg6tRVqork5GTeeustcnNzq1uVSuWZZ57hmWee8ckrESEpLoZG6YnkFBaRkV3I1r02SHyUMSTERpOaGEtibDTpSXEBDVGlmGPaAGKMYebMmRWun5aWFjSYe6SUONehKq/Guox9LN6Qxe7c/WTm7Gd3biG5+4u5+4LWtKxbowo1URRFURSlKhCxMSv7t2vAhe2O9+anJcaSnV9UjZopinKsU+0GKce41M85bAikGmM80ac/E5E8Y8yjwAPGmCyst9PfsN5dz7maegkYDfzbGPMYcAIwDnhKRLJRjkouuOCC6lahShARoow1ptRIiCUlPoaCAyVER0FsdFREAbGPdQ+pg+Wmm26qtLaKSyqtqbC5ZdoyVm2zj8TkuGhqJsWxZU8+Z7eorQYpRVEURTkKyd1fTMGBEmonx/nkpybEsDdfPaQURak+qt0gBdQFpvvleY6bAxuAR7EGqHuB2sASoJeI7PBUEJEsY0xPYDIwG7vj3tNYo5SiHNGUCD5GJ2MMiXHRFWrLAMewg9RhhddDqgqvx568/QxofzyPX9aOhNho9heVcOLYz8nb77+RqaIoiqIoRwOZOYUA1E6J98lPTYzVJXuKolQr1W6QEpEN2HfkUDICTHRSKLmVQI9KU05RDgM8OxD4BzE/qDYrrSXlYPDGkKrCPnMKi6iVHEdCrDVoxsVEERttyC1Ul31FURRFORrZlbMfgNop/h5SsWSrh5SiKNXIkRDUXFGOacRJIcNCRYAxx3YMqcMJj4dUVblIiQh5+4tJ8dvRLykuRj2kFEVRFOUoxeMhdZyfh1RaYizZBfqDlKIo1YcapBTlMMdjPIokTlQoDOohdbhQ1R5ShUUlFJcISfG+yz2T46LVQ0pRFEVRjlIyc4N4SCVqDClFqXJ2/wFPnQx7NlW3JocFapBSlMMcx2ZRiR5SqEXqMMFzbavqcni8oJLj/Dyk4tVDSlEURVGOVjweUrXKBDXXJXuKUuVkrITszZD1R3VrcligBimlWig4UKweGWGiHlJHL1Ud1NxzzyXFBfCQ2q/3o6IoiqIcjezK2U+NhBjiY3z//qcmxlJYVELBAf1RSlGqjPw99rNE7ztQg5RSTezILuDP3XnVrcYRQeV7SBlETVKHBZ4le1VlIvQYnZIDxZAq1D+KiqIoinI0kpm7nzp+8aPAGqQA3WlPUaqSgr32Uw1SgBqkjhnGjRuHMcabjj/+eAYNGsTvv/9+SPu97LLL6N69u48ederUYX9RCQeKSyguKQlZf//+/YwbN45ly5ZVmk6TJ08O29vooosuYvz48d7jl19+mV69elGvXj3S0tLo0qULc+bMKVNPRHjkkUdo3LgxiYmJnHvuuWXOYd26ddxwww20b9+e6Ohon3Fyszsri7+PGUXzRvVJSUmhb9++rFu3Liz9c3NzGTduHK1btyYhIYG6dety84irWLd2Tch6kyZNwhjDZZddVm4fr7zyCh999FFY+pRHcXExjz32GOeccw61a9emdu3a9O7dm8WLF5eRLSwsZMyYMdStW5fk5GT69+/Phg0bfGTCvV4vvPAC/fv3p3bt2hhjmD9/ftg6BxrjK664glWrVvnIZWdn8+CDD3LGGWeQlpZG/fr1GXXtMDasX+fjITV9+nRat25NcXHl/5HKdYxO/gap5Hj1kFIURVGUo5XMnEJq+y3XA0hNsP8PZOfr/wCKUmV4DVJ634EapI4p0tLSWLRoEYsWLeKJJ55g2bJl9OzZk9zc3CrTYeTIkXz55ZccKLZv4IVF5Rukxo8fX6kGqXD54YcfmDdvHrfccos3b+LEiTRv3pyXX36ZGTNm0LJlSy644AJmzZrlU/fRRx9lwoQJ3H333cyePZuUlBTOP/98tm/f7pVZsWIFn332GSeeeCInnnhiUD2uunIYCxd8w2NPPMm7775LZmYmPXv2JDs7O6T+OTk5dO/enWeffZaRI0fyxRdfMHnyZHZs28rlfc/ju+++C1gvIyODhx56iOOOOy6cYapUg1R+fj6PPvoonTp14p133mHq1KnExsbStWtXli5d6iM7evRo3nzzTZ544glmzJjBrl276NWrFwUFBV6ZcK/X22+/ze7du+nTp09E+gYb4y1bttCpUyefMf7zzz959dVX6dOnDzNmzOCll15i544d/GXA+WzeXBrUcNCgQYgI77zzTkS6hEOex0PKb8me7rKnHAn8mZnHs1//pruEKoqiRMiunMIyAc1BPaQUpVpQg5QPMeWLKEcLMTExdO7cGYDOnTvTpEkTzjnnHD777DMGDx5cRr64uJji4mLi4sr+AasojRo14vjjG7J8q70RCw+UkFR5zVcqzz77LAMHDqRWrVrevJ9++ok6dep4j3v16sVvv/3G008/zYABAwAoKCjg0Ucf5d5772XUqFEAnHXWWTRr1ozJkyfz8MMPA9b7auDAgYD1JNu1a1cZHRYtWsTXc7/ilfc+ZtjF/UiOj+HMM8+kefPmvPLKK9xxxx1B9R87diz/+9//WLp0Kaeeeqo3/4zufRh0UV+GDRvG2rVrSUhI8Kl377330r9/fzZtqvqdHxITE1m/fj3p6enevJ49e3LiiScyefJk3njjDQA2b97Ma6+9xuuvv85VV10FQLt27WjevDlTp05l5MiRQHjXC2DhwoVERUWxfPlypk2bFra+wcb4kksuoUePHj5j3Lx5c37//XcSExMBKCkRardoT5/Op/Lu22/R/pEJAERFRXHVVVfx3HPPMXz48AhHMDQeD6mkuAAeUhrTTTnMeeiTFcxdlcGA9sfTrE5ydaujKIpyxJCZs5+OzWqVyU9NcAxSGthcUaoONUj5oB5SxzAdOnQA8C5zGj58OB07duSjjz7i5JNPJiEhgR9++AGw3h1DhgyhVq1aJCUl0adPH9as8V32tWnTJvr160diYiLNmjVjypQpZfocN24cdeuWet4UFBWTmZnJDTfcQIMGDUhISKB169ZMmjQJgBo1agBwzTXXeJcbevQtKCjgrrvuonHjxsTHx9O+fXs+++wzn/4KCwsZNWoUNWvWpFatWtx+++0cOFD+H919+/Yxc+bMMkvW3MYND6effjoZGRne44ULF5Kdnc3ll1/uzUtOTuaiiy7i888/9+ZFRZV/+y1btoyYmBg6du6CZ5VhvXr1aNeuHZ9++mnQenl5eUyZMoW//OUvPoYSgLi4OG656342bdrEv//9b5+yxYsX88EHH/Doo4+WqxtA9+7dWbp0KW+99Zb3+rz55puANWiOGzeOJk2aEB8fz8knn8y7774bsr3o6GgfY5RH35NPPtlnjD3L7i699FJvXsOGDenatavPGIdzvSC8a+FPqDGOjY3l4Ycf9hnj5ORkrzEKoFiEtPR0GjRqzM6dO33qDxo0iJ9++only5dHrFdInb0xpNRDSjmyWL5lL3NX2ft2o8YfVBRFCZviEmF33n7qBFiyl+Z4SO1Vg5SiVB1qkPJBDVLHMB7DTv369X3y7rrrLu69914+++wzmjdvzu7du+natStr1qzhpZde4oMPPiA3N5fzzz+f/Px8wMZMGjhwIMuXL+e1117jqaee4plnnmHRokVl+nUvttibnUv37t356KOPeOCBB/jss88YM2YMW7duBeCbb74BrCeKZ7lhgwYNAOtV9Oabb3Lfffcxe/ZsOnXqxIABA3yW991zzz1MmTKFBx54gH/9619s3LiRJ598styxWbhwIfn5+Zx99tnlyi5atIi2bdt6j1evXk10dDStWrXykTvppJNYvXp1ue25KSgoICYmhujoaKJcca/i4+PLxChys3TpUnJzc7n44osDlnfs3JWaNWuyYMECb56IMGrUKO66r5fFeAAAIABJREFU6y4aNmwYln4vvPACbdq0oV+/ft7r079/fwD+/ve/M3HiRK6//npmzZpFly5duPLKKyPyQAJrVFy6dGmZMW7UqBEpKSk+suGMsf/1qijljXG3bt3KjLGbkhJhd+YuNm1Yz4lt2viUnXTSSaSnp/P1118ftJ5uSnfZ8/OQcnbZ06VQyuHK5G/WER9j/2XZmFl1y8wVRVGOdLLy9iMCdWoECmruxJAq0BdjRakyPAYpCR265lhBl+xVlM/vge2/Vk/f9U+FvuF5sPhTVGT/4Kxfv56bb76ZGjVqcP7553vLMzMzmTt3Lqeddpo374EHHiA3N5dly5Z5l6916dKFZs2a8frrr/PXv/6Vzz//nJ9//pnvv/+eM888E7AeWC1atChjmPFYpBJjo3n3rbdYsWIFP/30k7fPHj16eEU7deoEQIsWLbzLDQG+/vprPv30U+bPn0+3bt0A6N27N2vXrmXixIlMnz6dzMxMXnrpJcaPH8+YMWMA6NOnT1jGiKVLl1KnTh3q1asXUu7111/n559/9jFyZWVlkZKSQnS0rxdKeno6eXl57N+/P+xlkC1btqSgoIDfVq2gdX17/vn5+Sxfvpx9+/YFrbdlyxYAmjZtWqbMY9dq2rQpmzdv9ua/8cYbbN++PeQyQH/atm1LcnIyxx13nM/12b17N5MmTWLs2LGMHTsWsGO/efNmxo0bx9ChQ8PuY+LEiWRlZXmX4YEd45o1a5aRTU9PJysrK2hbga5XRQk1xh78x9hNsQhPThhLUlIyFw8qu2S2Xbt2/Pjjjwetp5tcxwsqxX+XvfgYRCD/QHEZY5WiVDdrtu/jixXbGXVeS1777g827FIPKUVRlHDJzNkPQO3kAAYpXbKnKFWPekj5oB5SxxCZmZnExsYSGxtL69atWb9+Pe+//77X4wjssie3MQpg7ty59OrVi9TUVIqKiigqKqJGjRp06NCBJUuWAPDjjz9Sr149rzEK7Mu4Z1lgIFISYlj07QJOP/30Mn2Wx9y5c6lfvz5dunTx6lRUVETPnj29Ov36668UFBR44zSBXZrlPg7G9u3bAy73crN06VJuueUWbr31Vs477zyfskC7+Hm8T8Ld4Q+sEadps2ZMuPd2flu7lm3btnHjjTeyd+/eMgavcDHg3dXNo8vevXu57777+Oc//+mzrKyiLF++nLy8vDKxya644grWrl1bZslcMD799FMmTpzIY489RuvWrX3PI8gYBxvfUNfrUBJMn1deeolP//0BDz7+LOm1apcpr1Onjk8Q/Mogr7AIYyAh1vfR7wly7okxpSiHE5PnrSM5LpoRXZvTtHaSekgpiqJEQGZOIUDAoOYJsdHExURpUHNFqUoK9thPNUgB6iFVcSrooVSdpKWlMXfuXIwx1K9fn+OPP77My3Igj6Bdu3bx/fff8/7775cp69mzJ2ANOHXr1i1TXrdu3TKePALEREWREBvNnj27qedaMhguu3btYvv27cTGxpYp8xhqPC/z/noF0tOfgoIC4uPL/pLkYf369fTv35+ePXuW8bZJT09n3759FBcX+xiN9uzZQ1JSUkCdgxEXF8fLr7/DyOH/j1NPtp5dXbt25aqrrvIuZwyEZ8ndxo0bad++vW+hMQjCxo0bvR5ojzzyCI0bN6Z3797s2WMfkkVFRRw4cIA9e/ZQo0aNiAxg27ZtA8rOJ89xVlZWuddh8eLFXHHFFdxwww3cdtttPmXp6elePd3s2bMnoOdUqOtVUUKOsYN7jN3MmjWLO/52G7fdN46efS8k0Eq5+Ph4nx0DK4Pc/cUkx8WUue89XlE2xlTwea8oVc3vO3P45JetXH/uCaQnx9GsdjK/ZQT3DlUURVF82ZVrPaTqBDBIgY0jpR5SilKFqIeUD2qQOoaIiYmhY8eOIWUCeXPUqlWLAQMG8MADD5Qp8wQdr1+/fkCvl4yMjAAeN0JcjCE+Joq0mrXYunlD2Ofg1qlhw4Z89NFHQWU8sbEyMjJ8dsoLxzunVq1aAQ0envp9+vShadOmvPfee2UMNW3atKG4uJh169b5ePWsXr2aNn6xgsLh9A4d+eS7n4jL2UFcXCwtWrTgwgsv9Fki50+HDh1ITk5m1qxZPrvJgfWQ+umHhezZs8cbI2vNmjUsWbKkTEBxsMafb7/9lq5du4ats8frLiMjg9q1S71/duzYAeBzPQKxdu1arwHpueeeK1Pepk0bNm3aRG5uLsnJpbttBRrj8q5XRQk1xgDffvutzxh7WLhwIUOGDOGakdcx/MbRAAhlLVJ79uwpd5wiJbewiKS4sufvCXKuHlLK4cbz82zsqOvOOQGApnWS+GZ1BsUlQnRU+N6miqIoxypeD6kAS/YAUhNiyM7XF2NFqRJKiqEwu/S7okv2lPLp2bMnK1as4OSTT6Zjx44+yWNw6dSpEzt27PDuygd2Z76ffvqpTHsiEBsdRXxMNGd2PZdf/reMX375JWDfnlhL/p4iPXv2ZPv27aSkpJTRyWN0O/XUU0lISODjjz/21ispKfE5Dkbr1q3ZunUrhYWFPvk5OTn069cPgE8++YSkpKQydc8++2xSU1OZPn26Ny8vL4/Zs2fTt2/fcvv2p0SsobBNm9a0aNGC3377jblz5zJixIigdZKSkhg5ciRvv/12mZ3aiouLmPzPiaSlpXl3qXv44YeZN2+eT2rfvj3nnnsu8+bNK7OLnJv/z96bR0ly1fee3xsRudTa1WqpWxtqCWm0gMxiwLYMuDFYEpKNF7AM2D4GGx7jBTjAzDlgGQQG2Sw2MHh47zHGNvaYZx4yBxuMeYbBgMBgP4N5LGYxICSQuhu1utW15BKRsdz5I+JGZlXlEpF5b1RlxPdzjk6pqrKyorMiI2584/v9/ur1+q6/z7XXXovFxcVtrwEA3HHHHbjyyitx3nnnYRQnT57EjTfeiMsvvxzvec97hgpIN9xwAwDgb/7mb9KvnThxAp/+9Ke3vcZZ/l7TMu41DoIAr3zlK7e9xgDw1a9+FT/1Uz+Fpz71qfj9N71l7PPfc889uPLKK7VtL5A4pBq770Nsd0gRsj/43pkOPvDFE/ilHz6Kc5fjC6lLDy2hF0Y4udHd460jhJD54HTLg22JdKLeTlYXaozsEVIUSowC6JBKoEOKTORlL3sZ3v3ud+PJT34yXvSiF+Giiy7C/fffjzvvvBNPeMIT8OxnPxs333wzHvnIR+KWW27BG9/4RjSbTdx22227YlmqR6lmW7Atgaf/wi/ifX/5p7jhhhvwmte8BldddRXuvvtufPOb38Qb3vAG1Ot1XHbZZbjjjjtw7bXXotls4hGPeASuv/563Hjjjbj++uvx8pe/HA9/+MOxubmJL37xi3BdF69//etx6NAhvOAFL8CrX/1qOI6Dhz/84XjnO9+JVqs18d/8+Mc/Hr7v4ytf+co2V9nTn/50fPnLX8af//mf46677sJdd92Vfk85lprNJl7xilfgda97HQ4ePIirr74ab3nLWxBFEV70ohelj+90Ovjwhz8MIC7I3tzcxPve9z4AwM0335yKJ2950+/jyCWX48yVl+ArX/kKXve61+FZz3oWrr/++rH/httvvx2f+cxncOzYMdx666147GMfi1OnTuHNb/2/8IV//We85z3/HQcOHAAQC0g7WVtbw7nnnosnPelJY3/P1VdfjY985CP4yEc+gkOHDuGyyy7DoUOH8JKXvAS333576sx7//vfjw9/+MNjp+x1u13cdNNNOHv2LN7+9rdvEyobjQYe/ehHAwAuvvhiPO95z8NLXvISSClx3nnn4TWveQ2OHj2KX/7lX05/JsvfCwA+//nP45577sG9994LALjzzjtx+vRpXHrppWNdhaNe4z/6oz/CZz7zGbz3ve9NX+NTp07hqU99KpaXl/HiF78Yn/vcv+LBdg+AwMWHz8EFP/KD6fO222184xvfwOte97qxr31eOl6QuqEGSR1SPd6pIfuH//LJb8O2BP73H3to+rVLD8WOyHtOd3DxQX0CMyGElJUzrR7OWarDGuEqXW3WsN7pFbxVhFQUFdcD6JBSSCkr/x+ATx47dkyO4mtf+9rI780Lr371q+WhQ4fGPuY5z3mOfMxjHjP0e8ePH5fPfe5z5eHDh2W9XpdHjx6Vv/RLvyT//d//PX3Md7/7XXnjjTfKZrMpL7nkEvmOd7xDPuMZz5CDr+2rbrtNrh08R57adKWUUt51akv+z6/dLZ///OfL8847TzYaDXnVVVfJt73tbTKMInn/Rle+/4N/L3/gB35ANhoNCUDefffdUkopXdeVt912m7z88stlrVaTR44ckTfeeKP80Ic+lP4+13Xlb/zGb8jV1VW5trYmX/jCF8o3v/nNMt71x3PttdfK1772tdu+hrgCa+h/g0RRJG+//XZ50UUXyWazKZ/whCfIL3zhC9sec/fdd498LvVvlFLK5/36b8nD518g6/W6vPzyy+Ub3vAG6fv+xO2XUspWqyVvu+02eeWVV8parSYByGazKd/53g9KPwjH/uyxY8fkM57xjIm/46677pJPecpT5OrqqgQg3/Wud0kppQyCQN52223y4osvlrVaTV5zzTXy3e9+99jnGveaHD16dNtjXdeVL33pS+W5554rFxcX5U033SS/853vbHtM1r/Xc57znKGPec5znjPx3z/qNf74xz++7XGf+MQnRm7LdY9/4rbHvv/975fLy8uy1WqN/d15j03P/H8+K295x2d3P8+JDXn05R+SH/7yiVzPR4gpTm+58opb/16+6m+/su3rJ9Y78ujLPyT/8p/v2aMtI4SQ+eJ5f/45eeNb7xz5/Rf91RfksTd9fOT3CSEaOfFFKV+9Gv/36bfu9dYUxrFjxySAT8ohWoyQw9p0K4YQ4pPHjh079slPfnLo97/+9a/jmmuuKXajSkrXD/Gt+7dwyTmLWFus48R6Fw+2e3j4hau7+qs2uz7uSaYpOYnVeG2xjsW6nWtS3bS89a1vxZ/+6Z/uimMVzX0PdrDlBbjmgtWZn+ujH/0obr75Zvzm//HbeMvv/y4cm6ld3ajX+LWvfS1uvfXWkY87sd7F2U4PAgIHFhxcNOD2ePazn42lpSX8yZ/8ydjflffY9NNv/yccWqrjXb/6Q9u+fu+DHTzxTZ/AH97ySPz8Yy7O/HyEmOJL967jZ/7zZ/Anv/JY/MTD+sMRokjimtv+Ab9y3VH8zk8+bA+3kBBC5oOf+y+fwVLdwbuf/8NDv//Kv/0KPvyV7+MLrxrvvCeEaODuTwF/8bT4/5/8KuDH/s+93Z6CeNKTnoQ777zzTinlk3Z+j1ejpFD8IAIQR/YAoOFYiKSEH0a7HrvlBbCEwNFDS1huODjb8XHXAy1861QLUQFC6gte8AI88MAD+NjHPmb8d40j7pDS81w33HADXnP76/H2P/g9vPeO3VMTyezccMMN+IM/+AO88pWvHDqZUhFGErYQEALbKs3vvfdefOADH8ArXvEK7dvW8gIsDu2QiiN77JAi+wXVZ3JgcXvniWUJHD20iHvOdPZiswghZO440+qNnLAHxJG9za4PmhQIKQBG9nZBQYoUihKe6kqQqsUXwl6wW5BquT6WGw4OLNRwyaElXHPBKs5ZqsP1Q0SR+ZPm0tIS/uIv/gLtdtv47xpHJCUsjY6w33zRi/HF7z2IW255prbnJNt56UtfiiiK8Mxnjn6NIynTPofBNeB9992Hd7zjHbjiiiu0b1fHC7E0dMpeLFJxyh7ZL6iJT6vN3SW8Rw8t4btn9va4TAgh88KZlodDy8Mn7AFxqXkQSXR9rgEIMU53YIo7S80BsNScFIwfRhAQcOz4QrzhxMKU60dYafYf5/khvCDadgK1LYGFRMAq6h7OU5/61IJ+02gkAL3TzRMRpLBXkQwjjGKhMRLb/w7XXXcdrrvuOiO/s90Lhk7ZazgWLEGHFNk/KIfU6sLu/fXSQ4v41DcfQBTJkSW9hBBCgG4vRLsX4tAYh5SavrfR9dOpu4QQQ2xzSHHdDdAhRQrGDyVqtkg7oBxLwLYEvGD7XZktL36Drgy5eK4akZRaO7PS6zfqUXtKJOO/hYBAES55KSU6vRBLQxabQggs1R06pMi+YbObCFIjHFJeEOH+LbfozSKEkLniTNsDAJy7NMYhlRxnlTOVEGIQJUjZdUBy3Q1QkMoMc9V66IVR2h8FxBfCTceG52+P7LXcAHXHSiN9/cfHH6v055ASWiN76fNqf0aShyiSsK3p3Wp5j0leECGMJBYbuyN7ALDYsOmQIvuGLTeAbYm032yQSw8tAQDuOc0eKUIIGcfpVg8AxjqklBNVOVMJIQZxN4DGgViQYocUAApSmajVauh2u3u9GaXA3yFIAXFcaLBDKpISLS/AypA74ypuViU5JZISOuWoKop6+5Ew6QabVmvsdruo1Ya9R4bTTlyHwxxS6uvtHk+MZH+w6fpYaTpD3aFHD8UTKdkjRQgh4znTih1SYzukUocUBSlCjONuAAsHAMtmZC+BglQGDh8+jOPHj6PT6dApNQNSyjiy52y/wGjUbARRhCApPO94ASIph8b1qidHxa+bToeUYIfUviCSsUNKIJ84KKVEp9PB8ePHcfjw4cw/10nEpmGOEyBxSHk8MZL9wWbXHxrXA4AL1xZQty1O2iOEkAmcUQ6ppWwdUoQQw7gbQPMAYDkUpBJY0JOB1dVVAMCJEyfg+zxYT0sYSXx/w4W7WMP6gNjk+iFOt3qIzjbQcCxsdH20vAD2ZnOXENPpBXiw7UOsN+DY1dBTT264aDoW2qdGLybyoF5vebaBulON13A/cvJsF62mAzcIYQmB7gOj717upFar4ciRI+mxKQvtJI63PKKXbbHupI8hZK/ZdIOhheZAPODiIecs0CFFCCETON1WDqlxkT06pAgpDHcDaK4BrVMUpBIoSGVkdXU118Uf2c2X7l3Hf/rLz+Cdv/JYPO6aI+nX732wg5970yfw+qf/AJ79Q5fgxrd+Cueu1PHfnv+Du57jg186gRd/8H/hYy/7MVxxeKXIzd8znv3aj+Jpj7gQr/vZa7Q836e/9QD+01/9K/7616/DIy89R8tzknx0egFuuu0j+O2brsY/fPX7WG44+MvnPcro71SF5YsjBKmlup12TRCy14xzSAFxjxQdUoQQMp4zrR4W6/bY6XkrTdUhxYtjQozjbgDnXAYIRvYUtEeQwji5EfdwXbjW3Pb1i9YW0HAsfPtUCyc3uviP+7dw7Mrzhj6HnTimwmjot0uJ64do1vS9VVWRdhAysrdXtJJF31LDgWMJhJH5v0W/Q2pUZI8OKbJ/2HTHC1JHDy3hu2fajNETQsgYzrQ8nDumPwoAaraFxbpNhxQhReCuD0T2KnRBOwYKUqQwTqzHI7ovPLCw7euWJfDQ85Zx1wMtfOqbDwAAjl05vBtHpfSKuIDfD0gp4QURmrXhIsI0qFL5qryG+5GtRBxaaTqwLYGggL+FmqA36i7pUt1Gx2OpOdkfbI2J7AHApecuotML8cCWV+BWEULIfHGm3Rsb11McWKixQ4qQIkg7pOiQUlCQIoVxYr2LZs3C2uLuu95XHF7Gt0+1cOc3H8D5q01ceWR56HPYVrzLRhW5K94LI0gJrYKUckj5VOX3jMGJd45lFeSQisWmpcYIhxQ7pMg+YrPrj5i0GnP00BIAMLZHCCFjON3q4dDS5I7K1WYNmy4FKUKMEgZAr8VS8x1QkCKFcXLDxYUHFoaO8b7ivGUcX+/i0988jWNXnjf0MUD1HFKuH4tGDY3l404iSIWM7O0ZKrK3vAcOqaVRHVING51eODIC9cEvncBr/+5rxraPEEUQRmj3wgkdUosAgHtYbE4IISM53fJwbgaH1OqCg80uL44JMYq3GX+kILUNClKkME5sdHHh2sLQ711xeBlSxlGmY1cN748CkE7dCyvikPL82NWi0yHlJC6zIkQQMpyW1594F3dImXertXuJQ2pEZG+x7iCM4ojoMD761e/jv/3P77KzhxhnKxFsx0X2LlpbgGMJTtojhJARRJHEgxkje3RIEVIA7nr8sbmWCFKsygAoSJECObHexQUHmkO/d/nhOH5hWwKPv+Lckc+h4mZRRcQUIw4pWxXDV+M13I8MClK2JQopmG97AYTAyIJ8VXbe6Q0/OW50fXhBxEl8xDjqomicQ8qxLVx8cIGRPUIIGcFG10cYyUyRPXZIEVIA7kb8sXkAsCw6pBIoSJFC8MMIp7Y8XDDCIXXZuUuwBPDoh6zhwMLoixA1Za8q7h430O+QSqfssUNqz0g7pBJBqohOtLYXYqnujIzDLiZRPrVtOznbiYWo+85SACBmUbGR1THnAqA/aY8QQshuzrTjoQ+ZHFILNU7ZI8Q02wQpB5B0SAEUpEhB3L/pQkrgorXhDqmGY+N5T7gMz3/iQ8c+j1U5h5SJyF4iSLFDas/Yqyl7owrNgX6Ub5RDar0TL1TvO9vVv3GEDNB3SI2O7AFxj9R3T3cYIyWEkCEoR/O5y1lKzR1seUFl1teE7Ak7BSk6pAAA41d7hGjixLoLALjgwHCHFAD8zk8+bOLzKHdPVTqkVGRvVMxqGpykGZ6Rvb2j7QVwLIGGYyUdUgU4pHrhyP4ooD99b9SkvQ0KUqQgtpQgNcEhdem5S9jygqQjZfIFFyGEVIkziSCV1SElJdDqBWPj0oSQGeiqDqkD7JAagA4pUggnN+KL2AtHOKSykpaaV0RM8QxE9pRDymdkb89ouQGWGnF8zrasQtxqHS/A4jiHVBLZ63i7T45+GKWuruPrjOwRs6jI3spEh1TcPcgeKUII2Y2K7GVySCU3ANTNJ0KIAbY5pGw6pBIoSJFCyOKQykJaal41h5Sjv0OqKqLefqTlhVhOBKCiHFItL8DiGIfUYn20Q2qwV4IOKWKazYwOqaOHFgGAPVKEEDKE060ehAAOLmabsgeAk/YIMYm7AQgLqC8zsjcABSlSCCc3ujiwUEtdGNPipGKKjq3a//Q7pPS9VWtW/FzskNo7Wp6fClK2XVSHVJhO0htGv0Nq98lxPRGkLEFBiphns+tDCGB5jIAKABcfXIQl6JAihJBhnG55OGexnt6IHMfqQny8VQ5VQogB3A2gsRpP2BN0SCkoSJFCOLHexQUHZovrAdWL7ClBqqHTIWVX6zXcj7S9EMvNQYeUeYW13QvGCsIqztceEtlbTybsXX7eMu47yxJpYpZNN8BKw0mHWIyi7li46OAC7jlNhxQhhOzkTMvL1B8F0CFFSCG4G3FcD2CH1AAUpEghnFh3ceHabHE9oIKRvcBAqTk7pPacLa8vDhU2Zc+bUGo+ziGVdEpce9EBuH6EM+2emY0kBLFDalJcT3HpoSVG9gghZAhnWj0cWso28OGA6pDqUpAixBjuBrCwFv+/ZVOQSqAgRQrh5EZ35kJzAEgGxBVyAb8f8JRDSmOpedohxcjentFyfawU3CHV7o0vNV+ojXNIxQvUh1+4CgA4ztgeMcimm33K0wUHmvj+pmt4iwghZP440+5ld0glgtQmBSlCzLHLIcXIHkBBihRAtxfibMefudAc6Ef2oooIUiY6pJRDqiqi3n6k7YVYSsQhqwBBSkqJtheMdUhZlsBi3R7bIXXtRfFJlD1SxCSbrp/2mUziyGoTD2x5jCATQsgOTre8TBP2AGCl4UCI+IYAIcQQFKSGQkGKGOfERnzxqschVa3+Iy+IIARQt/W9VYUQSUyMkb29ouUFWG7EdyOLcEh5QYRIYqxDCgAW6w5aQxxSG514Us81F8QOqfvOskSamGOz62Mlo0Pq8GoTkYy7UgghhMR4QYgtN8ChpWwOKcsSWG44dEgRYhJ3nYLUEChIEeOcXI/jFBdqdEiFVemQ8kM0HRtCTJ6QkoeieovIbqJIot0LsJyIQ7ZlIYik0aLwthef8JYnTLlcaox2SB1YqKX/0SFFTLKVI7J3ZCW++3//JgUpQghRPJh0PR7K6JAC4mJzClKEGMTdAJoDHVKS5gCAghQpgJOJQ0pHZC8tNa+ImOL6kda4nqJmCXZI7REdP4SU2DZlDwBM7tKdXux6WhwT2VPfH9YhdbbjYy3pl7j44AIdUsQocal59sgeANzPHilCCEk504oFqXMzdkgBcbE5p+wRYoigB/idAYeUTYdUAgUpYhzVg7Q0IS6UBXXxXimHlMZCcwUdUnuHcisNTtkDYDRC2VK/sz5+X1oa1SHV6WFtMV7UXrS2gOPrdEgRM4SRxJaXwyGlBKktClKEEKJ4IIkx53JILTjY7PICmRAjeJvxR0b2dkFBihinlzhxHA09SFbVHFJBhIaj/23q2BY7pPaILXd7fM4poBdNiUyLEyJ7iw0H7d6QDqmuj7VF5ZBaxH1nu0YjhqS6tJL3h5r4NIlzl+sQgpE9QggZZBqH1GqTDilCjOFuxB8pSO2CghQxjh/GwoeOYm5bVKvU3JRDqogibTKcnX1OdgFTD1UML5NDyhvmkNoe2eskkzMJ0Y26GFptZovsObaFc5cbeIAOKUIISTkzlUOqhg12SBFiBnc9/ph2SDlAtPsmcBWhIEWMEySClGPPXsxtpZG9mZ9qLnD9EA1DglRQlRdxn9HyRjikDP49lENqaZJDqu6kfVODDEb2Lj4Yd8GxR4qYIBWkMjqkAODwSoMOKUIIGeBMu4e6Y028ETXIgQWWmhNijF0OKXZIKShIEeOkkT1rdkHKTuNN1YibeUGEpoHInm2zQ2qvaO3skEqcgyZ70foOqclT9to7OqSCMMKmG+BAIhBclApS7JEi+lH9JSsZHVJA3CPFUnNCCOnT7YVYqueb0rzarKHdC9MbyYQQjewUpIRNh1QCBSlinCCMULetXCfFUfQjezM/1VzgGYvsWRSk9gjVkaMuuIuIobbTDqnx+9Ji3UFnx5S9zWR7BzukAOA4BSligH5kL7tD6sgqHVJmLsIBAAAgAElEQVSEEDKIH0ao5azKUNNNVdclIUQjXRXZY4fUTihIEeP4YaQlrgcAVrLHRhUpVHb9CM2agVJzS1TGZbbfUOLQzsheMR1SExxSdRu9MEIv6O8b6524GPVgEtk7sFDDStNhZI8YQcVFDuSK7DVxpu2lfYWEEFJ1etMIUsmNAPZIEWKAYaXmMgQqck07DgpSxDh+KHOfFEdRuVLzwIxDyrYEfHZI7QnqzuPSjlJz0x1SlsBEcVNN4esO9EitK4FgsS8QqEl7hOhGOfLyOaSakBI43aJLihBCACAIJeo5Kx/UjQBO2iPEAO5GHNOrL8WfW8lNYsb2KEgR80xjGx5Fv0OqGmKK64doGOiQcmxO2dsrWl6Ami3Sv6tyDwYGHWttL8RS3ZkYm11OIn2DPVIbyTS9tQHHykVrCxSkiBG2kguh5VwdUvEUKcb2CCEkxg+j3N2tapiE6vIjhGjE3YjdUWotbiWGA8b2KEgR88SClJ7InhAClqhaZI8dUmWi7QVYavTFoSJE1k4vmNgfBcQdUurxivVuHNlTU/aAeNLefWc7kBV5H5Li2OwGWGk46fsiC0dWmwDAYnNCCEmYpUOKDilCDKAEKUXqkKIgRUGKGEdnZA+IL+Cr4u5xjZWas0Nqr2i5QdofBRTTIdXygon9UUA8ZQ/od04BwNn2bofUxQcX0O6F7Jkg2tl0/fQufVYOJw6pUxSkCCEEQDzhupbTYc8OKUIM4m4AC2v9z5VDSjKyR0GKGEenQwoALCEQVsCZIaWEF0RoGojssUNq72h52wUpO2nqN+uQCnM5pNrbHFI+hMA2kUBN2mNsj+hms+unEyizcmipAUswskcIIQo/iFDPufZOO6QoSBGin5EOKQpSFKSIcXR2SAGJQ6oCYoqXTDprmHBIsUNqz9gpSBUzZS9IxaZxKBdVZ8AhtdHpYbVZ2xahuvjgAgBw0h7Rzqbr5yo0B+JzwnkrDZzaokOKEEKAuJcy79p7sW7DtgQje4SYYJcgxQ4pBQUpYhztkb2KOKQ8Pxak2CFVLlSHlKKYDqlwmwg2isUhpebrXR9ri9sFgr4gRYcU0ctmN0h7TPJwZLVJhxQhhCT0Qgkn59pbCIHVpsNSc0JM4K6zQ2oEFKSIcfwwSieJ6cCyBKIKiCluELtUmjUDU/YsgSBkh9ResOUF2yaIFSFIxQ6pycKmckgNdkitd/xt/VFAbOtfbjgUpIh2pnFIAcDhlSZLzQkhJGGayB4Qx/PZIUWIAVhqPhIKUsQ4RiJ7FXBIuX4iSDn6HVJVKobfb7S9AMv13YJUYLBkvt3LVmquHFKdHQ6pAwMT9oD4Lmo8aY+CFNHLlhvkLjUHgCOrDZzaokOKEEKA6dfeBxZqjOwRohvfBQKXgtQIKEgR4/ihRF2jIGUJgSqYe1xfdUjpf5vWbEb29oqWu90h5RQR2fMylprXdk/ZW+/0cHBxt0AQC1LskCL6iCKJLdfHas5ScyCO7D3Y7sELWA5KCCHTClKrzRpLzQnRjbcZf2wOTNkTqkOqAhe1E6AgRYwTaI7s2RaqEdmjQ6p0RJFEuxcO7ZAyJRBKKTM7pBzbQsOxtjukhkT2AOCitQUcP9uFrIBbkRRDuxcgkpjaIQUAD9AlRQghU/e3ri442HTp2CBEK+5G/JGl5kOhIEWM09Ncau5YVrUie0ZKzQX8KtjM9hmqLHxl25S9+L1hanKkF0SIJLaJYONYajjpdoaRxKa7O7IHABcfXMSWF7D8lGhDXQStTOGQOrzaBAAWmxNCCJRDaooOqSY7pAjRTipIDTikGNlLoSBFjBOEkd7IXkUcUl6gpuwZKDW36ZDaC1QUrkiHVMsLkt+ZTdhcrNvoJNu55fqQEkMdUmrS3r2M7RFNqJjINKXmR1ZiQeoUi80JIWS2DikKUoToxV2PP7JDaigUpIhxdE/Zs0XFSs0NOKRsix1Se0HLixd52zqkbLMdUkpcWswQ2QPiSXvKIbXeibd3bWiH1CIA4Pg6i82JHlJBaorI3uEkssdJe4QQMktkrwYviNI1KCFEA0Mje0qQ4nuNghQxzrQnxVFYlqiEmOKadEhZAgEje4WzlUSSlgfcSqan7Clxaame0SHVsNHpxSfH9e5oQeqixCHFSXtEF+r9MY1D6pzFOhxL4H52SBFCCHphhJozTWQvvkjeYo8UIfroDnNIsUNKQUGKGGda2/AobCEqEdlTd6cahkrNqyDq7TdUZG+50b/gtkW8YIwMuf5UQfli1g6puoN2EvM72+kBANaGdEgdXKxhsW7j3gcZ2SN6UKPGVxfyd0hZlsDhlQZOsUOKEEKmrstYSW4IqOMxIUQD40rNJR1SFKSIcaYtVhxFVSbEeUqQMuCQqrFDak9II3vDOqQMlZr3RbAcHVKJQ2pDRfaGRKiEEDi0XGf5KdHGLB1SQFxsfmqLkT1CSLUJI4lI9oem5KHhxD/TC+iiJ0Qb7gZg1YDaQv9r7JBKoSBFjKM9sieEMTfJfsL1VWTPUIeUIQGEjKaVikPFdUgpt1PmDqnGYIfUaIcUANTYRUY0MsuUPQA4stpghxQhpPKoKcrTRPacZL3ONSIhGnE3YneUGHhPUpBKoSBFjKO91LwiDqm01NxAZM+xhLHOIjKalru71Nz0lL124nZayihIDU7ZW08dK8N/1mYXGdHIZtfHYt1OL4jycmS1ifsZ2SMVJIok/uU7Z/Z6M8g+oZecl6eJ7Kn1us81IiH6UILUICw1T6EgRYzjT5ljH4VlCVThxo0XRLAEtMYdFY4tEElUootrP5GKQwPxOWWpNzZlL+2QyiZsbndI+VhpOiMFAse24FfhzUgKYdP1p47rAbEgtdH1OR2KVI5Pf/s0nvXH/4Jv3b+115tC9gF+ErebJp1Qs+iQIkQ77gawsLb9ayw1T6EgRYyicuw6I3uOVZ1S82bNhhAGBKnElRNWIPq4n9hyA9Rta1tRvXGHlJfPIbVUd+D6EcJIYqPr4+CIuB4Q70ch76ISTWx2g6kKzRWHVxoAwGJzUjlUvHrL44UN6a8nphKklEOK7mdC9DHWIcXjNgUpYhR1QtMa2RMViewFoZH+KCDukAJ4B6xo2l6wzR0FDIiDhoSdTi+AJYBmxnJ8tX2dXoCznR7WFkc7Vhyb0xqJPra82RxSh1ebAID7WWxOKoZyBfKcToB+Ifk0a2/liKYgRYhGhglSQjmk6OqmIEWM4s+QYx+FZVXD2eP6EZqOmbeok7pyuOAokpYXbOuPAsw7pFpegKW6k9lpp8rPO70Q6x0fB4ZM2FPULIuLVqKN2CE1S2Qvdkix2JxUjW5PCVI8HpPZ1t7KIUVxkxCNuOt0SI2BghQxijqh6YzsVanU3JRDyvRkNzIcJQ4NkjqkDC3+Ol6YuT8K6DukWl6Aja4/csIeUJ33IimGuENq+sjekZXEIcXIHqkY3WQqr8/jMQHSbsdp1t6q15I3LAnRhJQjInt0SCkoSBGjmIjsWVWJ7PkR6oYdUiykLpaWG+waaW8b7vNq93aLYONIHVJeiPVOD2tjHCuOLbgPEW1sdv2ZHFJrizXUbQunGNkjFaMf2aOIQPpr72mG4qif6fHcTogeAhcIe0BjdfvX6ZBKoSBFjNILp5/0MQrbEogqENnzCuiQqoKwt59oeQGWGtvFISEELGFyyl5Oh1Q9fuyW5yel5mMie7bFu6hEC1JKbA4RbPMghMDh1QZLzUnlUIIUbxAQYECQmuKmplqvU9wkRBPuRvxx15Q9ClIKClLEKCqyp7NDqjKl5n6YuYg6L8qxRjGhWNpegOXG7gtux7KMd0hlZTHZvlObHiIJHJgQ2WPPBNFBpxcijORMpeYAcGS1yQ4pUjlShxTP6QQDkT1risgeO6QI0YsSpJoUpEZBQYoYxUhkryK9NV4QmeuQstghtRdsjRCkTHYxdXq7XVnjUA6p4+tdABgb2atxyh7RxJYbL8hmiewBcbE5BSlSNbqpQ4qCFJk1spdM2aO4SYgeUkFqRIeU5HuNghQxionInlORyJ7rh2g6piJ77JDaC0Y7pMw5jTpeiMV69v1IOaRSQWpMZM+xLNr6iRY2XR8AZnZIHV5pMrJHKoerSs15TicYWHtPEdlLpzBzXyJEDyMdUqrUnA4pClLEKP0pe3RI5cX1I3ORPXZIFU4YSXR64VC3km0LhIbuRuYtNVcOqROZBCk6pIgeNruJILUwfYcUABxebWDLC9D2uMAj1aGblprzeExmq8tQIhbddoRoYqRDipE9BQUpYhTfRKm5EKjCNXDcIWUosscOqcLpBfFrPexvalLY6Xj5Ss3VlD0lSB1YGN0h5djskCJ60OWQOrLSBACc2qJLilQHdkiRQWZZe6veKbrtCNGEux5/pCA1EgpSxCimpuxVYdHl+iEaU9its0BLdvGoPoZhbkFTHVJSSrR7w2OCo6g7Fmq2wPGzsSA1bsqebbCMnVSLza6uDqlYkGKPFKkSnLJHBpmlv7Vfal7+dTYhhdClIDUJClLEKEYie0KgAnoUXIOl5qpDimJCcfiJQ6o+RGQ0NWXP9SNEsu96yspi3UG7F1/gHJhYal6BNyMxjnJIrTRni+wdWW0AoCBFqkU/ssfjMek7sqeJ7Kkblj7Xh4Towd0A7AZQa27/ulAdUmHx27TPmBtBSgjxLCHEF4QQLSHEcSHE/yuEuHDHY4QQ4lYhxL1CiK4Q4lNCiEft1TYTQ5E9q/zdR1Ek0QsiNAwJUurvUfbXcT+h7lw7Q8YwW1b8N9dNuxffdVnKEdkD+j1SKw0Hzpj3blxqzn2IzI7qkJpVkDqcOKRYbE6qhCo1500mAvTXG9OsvYUQcCzBDilCdOFu7HZHAfHiH4KCFOZEkBJC/DSA9wD4LICfAfByAD8G4ENCiMF/wysAvArAGwE8DUALwMeEEOcXu8VEYUaQEghLPmVPRR1NlZr3HVJccBTFuDHMphxSHS8+yeV2SCURvwNj4npA0iHFfYhoYMsN0KxZaMw4WXS16WCxbuPuM21NW0bI/qfbU5E9Ho9Jf203bTqhZnOCLiHaGCVIAXFsj5G9+RCkAPwigC9IKV8opfxHKeW7AbwYwKMBXAUAQogmYkHq9VLKt0spPwbgFgASwAv3aLsrj28sslduQUr1QTRnvDgbBTukikddKAyL7JnqkEodUvXpHFLjJuwBSRk79yGigU3Xn7nQHIjv7v/41Yfx4a+cTI+jhJSdfocURQTSj+zVpuwhdWzBPjJCdEFBaiLzIkjVAGzs+FrSEAaldPwogFUAd6gHSCnbAP4OwE2mN5AMhw6p6VD2e3NT9hjZK5pxFnrHUFF/21ORvfwdUgBwcHH0hD0g3o+CSEKW/P1IzLPZDWYuNFc8+3GXYL3j4yNf/b6W5yNkv5NO2aOIQDCw3hhSEZCFmm3R/UyILtwNYGFt+Pcsh5E9zI8g9WcAniiE+BUhxKoQ4koAtwP4hJTya8ljrgYQAvjWjp/9evI9sgcEM+TYR2HKTbKfSB1ShiJ7DkvNCyedemMVN2VPFZPn7pBKHj+u0Bzo/1vK/n4k5okdUrP1Ryl+9PJDeMg5C3jv5+7V8nyE7GeklGmpOV0tBBhfEZAFup8J0chYh5RNhxTmRJCSUv49gOcC+GPETqn/AGADePrAww4CaEkpd8qMZwEsCiF23eoXQrxACPF5AI85efKkiU2vPL0ZRs+Owq5CZC9QgpThKXu09xeGei8Ms9DHDikTHVLxSS5vh5RyVE2M7NkUNokeNru+NoeUZQk887EPwWfvOoPvskuKlBw/lFCHYLpaCBALUkL013p5qdlWumYhhMwII3sTmQtBSgjx4wDeAeBtAH4cwLMAnAPgb4QQg1fsw66KxKjvSSn/WEr5WAD/dsEFF+jdaAJgoDeHkb1cqMheY8r8/yTokCoedbdx2HvBuEMqb6l58vi1hfGRPRUHYG8JmZVNN8CKhg4pxc8/5iGwBOiSIqWnO9CVRocUAeL9oGZbEGJaQYoOKUK0IOVkh9QuL031mAtBCsCbAXxQSvlyKeUnpZTvBfCzAJ6EeOoeEDuhVnYIVACwBqAjpfQL21qSok5o40bH58WqVGSPHVJlYVyfmmNZRhZ/qkNqMW9kL2Opuc3IHtHElhtgOWfX2TjOP9DEk68+jL/+t/somJJS4w0IUnQ9EyBeb8xyI9hhhxQhevC7QOTTITWBeRGkrgbwxcEvSCn/A0AXwOXJl76BOMZ3xZCf/YbpDSTD6c2YYx+GLcovSHmBKjU365DihVpxjIuvmp6yl/dCfzGN7E1wSNlqPyr3+5GYpxeE2h2hz3zcJXhgy8PHv3FK6/MSsp8YdEjR9UyAeG03S1WGY3HKHiFacJMZbGM7pOiQmhdB6rsAfnDwC0KIawAsALgn+dJnAWwCuGXgMYsAngbgfxSylWQXqStkykkfw7AsgUii1JO9lEOq4ZjtkCq7sLef8IPR8VVTMdSOF8IS+aOfqUNqUql58m/hnVQyK34oUdcsSP34Vefh8EqDsT1SarZH9ngsJvF+MMswobpjcV8iRAfuRvyRDqmx6PPHm+UdAN4qhDiBWFw6AuA2xGLUhwFASukKId4A4FVCiLOIXVEvQyy6/d97sdEkjuzZloA1ZbHiMOwkEx9JQKPxal9hPrLHDqmiUa/1sEWibajUvN0LsFh3cvdILGYsNe+X43M/IrMxa8RkGI5t4ZbHXoz/+sm7cHKjiwsOLGh9fkL2A6pzEqAgRWJ6gZwtsscpe4TogYJUJubFIfVHAH4LwPUAPgDgTYgjfE+RUg6O0HkDgN8D8NsAPgRgFcD1Usr7i91coojv0uhVjdQ5tszuHs83Hdljh1TRjBvD7FgCoQGXUdubrpfnQOKMOne5MfZxNQqbRANRJBFEcqY7+qN45mMvQSSBv/78fdqfm5D9QLc32CHFYzGJXcuzrL0dmw4pQrSQClIHh39f2BSkMCeClIz5r1LKR0gpl6SUF0kpnyml/M6Qx/2elPJiKeWClPKJUsr/tVfbTeLeHN0XGcptFZU5sheYdUjZ7JAqnF4wutTcNnQ3suUFWMpZaA4ANz78CP7suY/FpecujX2cnQqb3I/I9KRdg45+y+slhxbx+CsO4b2fuxcRhVNSQtR6AQB87uMEs0f2arYZ1zYhlSOTQ4pr6LkQpMj8EoT673o7Feg/6ndImS01L/NruN9QBaFDp+zZZkrNW144lUOq4dh48tVHJj6uZrHUnMyOEsZ1R/YUz3zcJTi+3sXn7nnQyPMTspe4iUNqueFwyh4BEEf2ZpluHU/+5b5EyMxMFKTokAIoSBHDmIjsWUkfjokS6P2Cm0b22CFVFlTx97D3g21ZZqbseQGWphCkspKWmlOQIjOgBE3dpeaKh12wCgD4/qZr5PkJ2UtUqflK0+GxmABQnXzTr71rtoUe9yVCZiedsrc6/PvskAJAQYoYxjfgkEonxJX4ZOkFIWxLGOlUAfodUly8Fkca2Rty0e2YKjU3LUipUnPajckMjIuz6mB1IX4PbLpc9JHyoW5gLTcc+DwWE2iK7NEhRcjsuBuAswA4IzpZKUgBoCBFDDPrSXEYqSBVcodU05BbAADU0EN2/xRHGtmzhndImYnsTVdqnhU67YgO+oX/hgSpZlzSv9n1jTw/IXuJckitLtR4k4kAmL0uw7EtntcJ0YG7MTquBySCVDj6+xWBghQxisnIXpkLal0/NBbXAwAhBEsrC2bSlD0TLqNpp+xlRTntWI5PZkGVmpuK7DUcC3XbwqZLQYqUD3cgssdjMQGSgUIzHE9rluC+RIgOuusTBCmLDilQkCKG8UOZXrTqojIOKYOCFGDOlUOGE4QRhOjvv4NYloCJtV/bCw13SLEcn8yOiuzN0nkyDiEEVhccbDGyR0qI64cQAliqO7zJRAAkN4OHrDWyUrMtClKE6CCLQ0rSIUVBihjFn/EuzTBsUf6LYDcI0aiZfXs6lsXpaAXSSyz0Qgx3SOmOT3pBiF4YYblhTthMO6S4H5EZMB3ZA4CVZo2RPVJKXD9E07Hh2HS1kJhZ6zIcW/C8TogO3A1gYW3099khBYCCFDHMrJM+hmFZKrKn9Wn3FV6ywDSJY+sXQchoxt2xtA2Umre9+I6LSYeUWvDyIojMQhGC1GrTYak5KSVdP8RC3YZjWRQRCIBkoNAskT06pAjRQ6YOKa5NKEgRowQGIntORSJ75h1S7JAqknFuQcdAfLLtxSc4k4JUGp/lfkRmwAvMdkgBceHzFjukSAnp9iIs1GzU6JAiCbP2t3J9SIgmJgpSNkvNQUGKGGbWYsVhWBW4CHYLcEjZFi3ZReKPmXpjW/on2rQSQcpkqbla8Polfi8S86QTKI1G9hxG9kgpURF/h4NKSEKcTphxyh7Xh4TMhpR0SGWEghQxionInuqQikrskPKCCM0COqS4eC2OcQtEkw6pIqbsMfpJZsFPS81NRvZqjOyRUuL2QizU7KQXksdikgwUmmHtXbcFemEEWeJ1NiHG6bXjwvJxgpSwKUiBghQxjInInrpmKfPdG9cPjU/ZY4dUsfhhNHKBqCYe6lz8tQqM7LEcn8yCuohmZI+Q/LhBvF6oO3S1kBg/mLXUXN1s4v5EyNS4G/HHiQ4pRvYoSBGjmJiyZ1XAIaUWmCYxUaRNRhOMiew5BmKoxUT24n8PL4LILPTSUnO9btpBVhoOXD+CF3DhR8pFN3VICQS8yUQQH1Nni+wlE3S5RiRkeihIZYaCFDFKb8ZixWFUoUjZ9YuI7LFDqkh6Y8Yw2wYWf/1Sc3PCZv+9yIsgMj29oIApews1AMAWY3ukZHST9YJjW/BDvU5bMp8E0egbYFmoWZygS8jMuOvxx4ml5lyXUJAiRglCmZ7YdGFVYspeiIbhUnN2SBXLuD41E71oLS++41JIqTmFTTIDav9pGI3sxe8DClKkbHhJxL9m0dVC4pu14ayCFM/thMxO6pBaG/0YlpoDoCBFDBNH9gyVmpd40eX5ERqmHVLskCqUuENq1JQ9kw4pg6XmKrLH/YjMQC+J0RmdsteIHVKctEfKRtdPInuMUBP0XU2zlJr39yWe2wmZmsyRPQpSFKSIUfwwMlBqXu7IXhhJ9MIITcMOKXZIFYsfypHx1bRDSuOFRNsL0HAsoxf5Du/IEw2ou/C6+wYHUZG9TRabk5KhhqCkrhbeIKg06ZAIHQ4pntsJmZ5MDimbHVKgIEUM44dS++Qku+SRPdWnYnzKHjukCsUf2yGlnEZ6S81NxvWAAUGK+xGZgSJKzRnZI2Wl64dYqNs8HhMAAwL/LA4piw4pQmYmFaRWRz/GsgFJQYqCFDGKb7DUvKw3AV0/PjCZLzW3Susy24+ME6RMTdkzGdcDBqKGXLSSGVAi/Cx39Cex0mRkj5QPKWU8BMWxGLMiAPp//1kcpw47pAiZHXcDqC0Bdm30YxjZA0BBihhESokgktoje5Yot0PKDZQgZdghZQta+wvED0ZH9vodUvr+Hu0CBCkhRDJqvJzvRVIM6saFEAYdUs34vcDIHikTnnJU123GrAiAQcfp9GtvdXOAU/YImQF3fXx/FEBBKoGCFDGGurNiLLJXUjHF9VVkz+zb07YEHVIF4kfFO6SWG2ZFTSAWNilIkVkY5x7UxVLdgSUY2SPlotuLb2At1Oz0PUSHVLXREtljQT4hs+NuZBOkZFTe2E9GKEgRY/iGekHUlL2yrrnSyJ7hUnPHsrjYKBA/jEZGksxM2QuNO6QAoGZZvItKZsJE1+BOLEtgueEwskdKxaCj2kldLTyvVxlfg0PKYUE+IbOTSZBKrvUq3iNFQYoYQ4kd2iN7ydOV1d2jBKmG8Q4pOqSKxA/kyDHM6j2i8+9RRGQPAGyb+xGZDS8w75AC4kl7m3RIkRKxzSFlqd4fighVRnXyzXJMrVl0SBEyM911YGHMhD0AEIkgVfHYHgUpYoyehmLFYaSl5mXtkFKRPcMOKZsdUoUydsqeAZG15QVYKUCQciyLd+TJTIxzD+pktVnDFjukSIkYjPgzZkWAvtN6lmNq2kdGcZOQ6cka2QOAiA6pzAghjgkhPiSEOCWE8IUQ4ZD/qi3xkRR1Iqsbi+yVc9HlBcohZTqyR2dLkYwXpPQ7pIqYsgfEC9ey9rmRYjAxjXUYK00Hm10uUUh56PqDkT3GrIiuyB5LzQmZmVyCVLXXJpmvVoQQPwngbwHYAL4H4D8AVPvVI2MxF9mriEPKeGSPHVJFMq4nx9HcIRVFEp1eMR1StiW4H5GZ6AWR8Q4pII7s3ftgx/jvIaQo3AFBSh2HeTyuNn4S2RtVEZAFdYOA+xIhUxJFgLdJh1RG8lytvAaAD+AnpZQfNbM5pEyYiuyZmEi2n/AGSkpN4lgCAe+kFoYfRum+uxPdkyPbvfheQRFT9mq2xTHjZCaKmLIHqMge76OR8qAEqYWajS7i/+eUvWrT0+GQUh1SXCMSMh29Vjw9L2upecUdUnmOVtcCeC/FKJIVdSKrjbgInxar5JG9wTueJmEZdXFIKRFEcuQCMXVIabob2fbifagohxQje2QWeuHo94ZO4sgeO6RIeVCRvYW63e/94Xm90qhORz0dUtyXCJkKdyP+yMheJvIcrVoAHjS1IaR8+EF8ItN9oWGX3CHVLzU3e4FWs4S2iBgZT7pAHPE31b1PtzzlkCqi1Fxw0UpmoheEhUX2Wr0AEY97pCQMDkGppaXmvEFQZYI0nTBLZI8dUoTMRGZBig4pIJ8g9Y8ArjO1IaR8mJ6yF5a2Q6ogh5RlIaSQUAj9ktHhC0TV9aBLIGwXKEjVbIsXQGQm/FAWNGXPgZTAllfthR8pD2mped1KY1a8QVBttET22CFFyGzkdUjJandI5TlavRzA5UKIVwohzI/DIXNPepfGUGSvrHe51R3PhmHHgGMLTuMpCCVIjSr41z1lTwlShZWal/S9SIqhqCl7q80aADC2R0qDNzw1fnwAACAASURBVHADKy2i5nm90ihBsjbDQKHUIcV9iZDpSAWptfGPY6k5gHyl5q8G8FUAvwvg14QQXwSwPuRxUkr5PB0bR+ab9KRoyiFV0otgNwjhWCIdu2sKx2KHVFFMcgvaQq9DaqtQhxSn7JHZ6AUFlZovxO8HFpuTstDt9UvNHcasCAYc2TNE9nT3WhJSOdxEImFkLxN5rlaeO/D/lyb/DUMCoCBFBmJKmgUpVWpe0vNkUSPQHTpbCiNIS0YnTdmbP4eUY1m8I09mohcWc8xLHVIuHVKkHHT9+AZWzbZSEYGRvWqjY+2tbp5R3CRkSlhqnos8VyuXGdsKUkr6MSXNkb3kHFvWyF4YSe2v2TBsy4KU8e+zC/h9VWbSAlH1NegXpMz2kAHxtrtBOd+LpBj8MCqkQ2qFkT1SMlw/Svsm+6XmPB5XmTSdMIsgxT4yQmZDCVKN1fGPoyAFIIcgJaX8rskNIeVj0mSxaSl7qXncp1KAQ2qgb8K2zAsXVSYVZ0f8XdU+rctp1PLiGEdRU/Z4AURmwQ8kI3uETEHXD1NBymGHFEF/vTGLyN8vNee+RMhUuBtAfQWwJ6zDhYrsVbtDyvwKkFQWY5G9kndIBaFMFwMmcUr+Ou4nesH4yJ7uv0XbC2CJuFfENI5t0dZPZoKRPUKmw/NDNGvxe4euFgIAfqBugM3eIeVzfUjIdLgbk+N6wECHVLUFqdy3z4UQPwLg+QAeDWANwAaAfwPwLinlZ/VuHplnTEX27JJP2fOjaOQ0Np30XTnlfB33E+qO9ShxVvffouUFWGo4KGIgKsvxyaz4BZWaLzfjJc9mlw4pUg66fpjeeFAl1nS1VBsda28hROJ+5r5EyFRkFqQY2QNyClJCiNsB/DaAnUe5RyGevPdGKeWtujaOzDemI3tlFVLCqFiHFONW5pnYIZUIkLqEnZYXFBLXA2KHVFnfi6QYemE000SorNRsC4t1G1t0SJGS0PVDLNSTyF5yHuHxuNr0Qom6bc18Q6pG9zMh00NBKheZlQIhxC0AbgXwPcQOqYcCWEg+Pj/5+suFEL9gYDvJHGLKISWEgBBAVNIOqSAsptRc9Rmxb8I8KrI3SmjULbK2E4dUEdQswX2IzIQfRmgU4JAC4tgeI3ukLLh+iKajSs3VlD0ej6tMEEbpvjALji0Y/yRkWtx1ClI5yLMCfBGA+wE8Tkr5Z1LKe6SUXvLxzwA8DsADAH7LxIaS+SN1hRjoBrFFeWNChZWas0OqMCaVjKZ/C00XEq0CBSmbpeZkBoIwQiT1dw2OYqXpMLJHSkPXj9BMHFJCCNiWoCBVcfww0rLurtkWbzYRMi3uBrCwNvlxqSBV7Q6pPEesRwJ4n5Ty9LBvJl//a8TxPUL6kT0DFxqWJUo7ZS+MZOqYMYnNyF5hTOqQsgw4pJYbxUxOjEvNuQ+R6UhHlBdQag4Aqws1bHl0SJFy4PkhmgPvHU49Jb1Qaukh5b5EyAx0s0b2kvcqHVKZcQB0JjymgymK0kk5CQxF9oDYIVXeUnOZxulM0h8RXc7X0RRnWh5+6U/+BQ9seZl/RkX2RndIJUX9mkTWthcW1yFlCYS8i0qmpGdoGusoVumQIiVisEMKUL0/PKdXGT+MRk70zUPNttLjMyEkB1EEeJv5InuSDqmsfBvATwkhhv5M8vWbAdylY8PI/OOHEYSAEbePbQmU9TwZhBFqRXRIpUXaJX0hDfHVE5v4zLfP4GsnNzP/TL/UvJgOqSIje47Nu6hkenrJiHLdwy9GscIOKVIiBjukgOR4zHN6pdEX2eO5nZCp8DYBSHZI5SDPEes9AK4B8AEhxP82+A0hxOUA3gfgYQD+St/mkXmmF0rUNEz6GIZtiXKXmhc4ZY93U/PR6cUnDc/Pfjdj8pQ91SE1f1P2arYFnxdAZEr6/Wrmj3kAsLrgYMut9sKPlIdub7tDyrHokKo6QbL2nhWHHVKETIe7EX/MJUhV2yGV54rlLQCeCuAnAdwkhDgB4CSA8wFchFjc+qfkcYQYdfrEDqlyLrqCKMKiY15MsFlqPhWdXnzS8ILsC7VgQk+OToeUlLLQKXtlfi8S80wSa3Wz2qxhs+tDSmnkZgkhReL6EZq1viBVt0Val0CqSU/TYBzH4pQ9QqYilyCVHL/pkMqGlLIH4HoAvwPgbgAXI56s95Dk898B8JTkcYRosw0PwxKitN1HQVSMQ0otWMr6OppCCVJuDodUb0JkT01H0iHseEGEIJLFOaSSRassqWORmGUvIntBJNHN8f4lZD8SRhK9MEKzNlBqbls8p1eceFKzng4pTmwkZAqmckhVW5DKdcUipfQBvB7A64UQywAOANiQUrZMbByZb3qabMPDsC2Ut9Rc04SUSfQdUlxw5CGN7OVwSKUukDF/V9vSI7K2vXj7lurFTdkD4oujIoRUUi4KLzVfiJc9W26AxTpnsJD5Rd0UWaht75CiiFBtfE0OKXZIETIleQQpQYcUkK9DahtSypaU8jjFKDIKo5E9IRCW1JERRpGRyYQ7YYfUdEwT2UsFqTEuEF3T6tpevH3LzdrMz5UF3YXspFqo40+9wMgeAGx2WWxO5hslSA1G9moWXS1Vxw+kFoeUQ4cUIdORClJrkx/LDikAMwhShEzCaGTPEqV1SBVVas4OqemYJrKnLrrHLRJ1OaRaiUNquVGMQ0r9myhIkWkoukNqpRkv/jhpj8w73REOKbpaqo0faXRI8bxOSH5Yap6bkX51IcR3AEgAPyGlvDv5PAtSSnm5lq0jc00cPTNYal5Sh5SuxcQkHHZITYXJyJ4OcVAJUkWVmqt4KYt0yTQU3SG1upA4pDhpj8w5qUNqcMqebcHnOb3S+GGkxXHqWBaCkMdJQnLjrgMQQGN18mNZag5gfIeUhViQGvX5KFgiQgDoy7EPwxblnewVGhTyBlG/g0JCPjqeiuzlcUhFsC0Ba8zf1dEkSLWLFqTokCIzMKnwXzeryiHFyB6Zc1w/fu80B8TcmsUpe1XHD/S47Gu2hR7ddoTkx92IxagsfcAsNQcwRpCSUl467nNCJuGHkbG73pYlEJXWIVVsZI9CQj7SDik/j0NqcqeDbodUUVP2+g4p7kckP35QcKl5kw4pUg7SyF6dkT3SR2+pOcVNQnLjbmSL6wEUpBLYIUWMEUTmnD663CT7kSCMCpmyVxuYjkay004je/kcUpMWiI6lZ1z33jmkuHAl+VEOqUbBkb0tdkiROWdoqbltweexuNL0dEX2bD1rEkIqRy5BSkX2qt0hNfMRSwhRE0I8WghxlY4NIuWhF5iL7FlCoKw3boouNeeCIx/dqRxSkxeI8+uQUtFP7kckP0WXmjccCzVbYLNb7buRZP5R56KFHYIUj8XVJgilHoeUJThlj5BpcDeAZob+KAAQyfFbUpDKhBDiF4QQdwghzhn42uUAvgrg8wC+JoR4vxCimKsgsu8xGdmLL97LeaI06SwbhB1S05FO2cvjkMrQ6eBomrLXTjqulurFTNnrl+NzPyL58YNkAmVBDikhBFabNU7ZI3OPm8Rdm7X+e8ehiFB54gnXs68hHZv7EiFT4W4AzbVsj7UsQFiM7OV47K8BuFpK+eDA194M4AoAnwDwZQA/A+BX9W0emWdMCiuWJVDWm4BBFKUX+SZhGfV0pFP2cjqkJt2x1CWytnsBmjWrkH0IGBA2uR+RKfAKLjUH4tjeFjukyJzj9oZH9ngsrjY9TbUPdNsRMiXuBrCQUZAC4h4pClKZeRiAz6lPhBCrAG4GcIeU8icA/BCAb4CCFEkwGdmzBRCVcNElpYwLsAtxSLFDahrSUvMghyAVyUyRPR2Lvy03KCyuBzCyR2ZDlZo37GIcfQCw0nQ4ZY/MPWmpeW17qTldLdVGVzqhZlvclwiZhjwdUgAFKeQTpM4DcHLg8+sQT+n77wAgpfQB/H8ALte2dWSu0TXpYxi6+nb2G+qfVIS7xWZkbyr6glSeyF6GUnNbzz7d9oLCCs2BfvcPF65kGtIOKQ0Rk6ysNmssNSdzz7BSc8eiq6XqZJnqmwVdNQKEVIooBLzNKQQpdkhlZQvA4Kt7DIAE8E8DX3MBrGjYLlICgkjPSXEYlhAIZflOlOrizC6yQ4oLjsxIKdPInpszsjepQ8oWujqkAizVixOk1L5aRoGYmKfoUnMAWF1wsMnIHplzukOn7NEhVWWiSCKM9JSaO4zsEZIfdyP+mEeQYodULkHqWwBuEkI0hBB1ALcA+LKU8vTAY44COKVzA8n8ksUVMi22JUoZ2VOCRBF9KkogoZCQHS+IUhdbLodUhgWibQlEGkTWlhdguVlgZC/Zj3wuXMkU9IIIQqCQQQ6KlUaNkT0y97h+PL118AaWY9PVUmX8SJ/AX7cFemEEWcKbv4QYYxpBig6pXILUHwN4KGJh6uvJ///Zjsf8MOKpe4SgF0pj0TPbKqdDSsXndBRSTkL9Di5es6PiekDODqkgmtghpStq0e4V2yFV45Q9MgO9ZES5EEWWmjssNSdzj+uH2ybsAfF5hA6p6qJuDGmJ7NnsGSUkN1MLUtVek2S+6pVS/gWANwBYRBzde3vyHwBACPFkAJcinrhHCIIoQt2Q06fsDqlJ8S4dsEMqP20vPmHYlkj7O7KQZQyzrl60thcW2iFlM/pJZsAPJ4u1ullt1tD1Q/RyiMqE7De6vRAL9e3DAOoOY1ZVRg2J0BPZ47mdkNzQITUVua5apJS3Arh1xLf/CcBBAO1ZN4qUA6ORvZJ2SKmFZDEOKS428qI6O9YWavkcUmE0USRybJErBjiKeMpecRPLasppx4sgMgW9QM9EqDysJJHWLdfHoeVGob+bEF24QbitPwpQRdQUWquKzk4+dW73w2jXfkYIGcFUgpRNh5SuJ5JS9qSUG1LKar+iJMU3GNmzLFHKC2C1mCjCIWVZApagHTsPKrJ3cKkOL1epebYOKW1T9gosNe93kfEiiOQnnsZaXFwPAFYXagDA2B6Za7q9EAs7BSnbgh9K9v5UFD9ZQ+hwnbIfkpApSAWptew/w8hedkFKCHGpEOJmIcTSwNccIcTvCiG+JIT4rBDi58xsJpk3pJTwTUb2hJ4C6P1GWGCpOaD6Jsr3Opqik0T2zlmsww3CzIt+P4xQnxDZ0zFiOYwkun6xkT3ltON+RKahF5pz0o5itRkLUpsui83J/OIGERo7BKkanc+VJo3sTVhvZCHth2StAyHZoUNqKvJctbwawE8DODLwtVcCeNXA53cIIZ4opfwXHRtH5pcwkpDS3ChvXW6S/Yay2tsFRPbi3yPobMlB3yFVg5SxCDNJaAKUC8S8Q6rdi09oK4VO2WOpOZmeXobCf92o98dmt9oLQDLfuL0QCztLze1+hJopq+rhaxyMo26M+iVcaxNiDHcDEBZQX87+M5YDyGp3SOU5Yl0H4B9VJE8IYQH4TQDfAHAJgB9C3B/1Ut0bSeaPfjm3ucheGc+R6YSUgkagc0R0PpTgc3CxDgCZO5/8UE5cIDqWNfPfQpWu74VDqowRWmKe2D1YsEMqjezRIUXmFzfYHdnriwi8QVBFeho7pNJJzHRIEZIddx1orAJ5RGHLrnypeZ4j1hEA3x34/FEAzgXwn6WU90kpPw/gAwAep3H7yJzSPymaiuyVs/soLTUvyDHglLSLyxTdgQ4pAJmLzbNE9iwdDqm9EKQ4iYfMQJZ+Nd0oQYqRPTLPdHvDS80B3iCoKuqmZhbn9iTYIUXIFLgb+eJ6ADukkE+QqgEYPCo9Pvn84wNfuw/ABRq2i8w5OkfPDkPHxft+RMWenIIcUrYGV06VaCeC1DmJQ8r1szqkJkf2HA37tCppLnLKHu+iklnYi1JzRvZIGej6w0vNAR6Pq0qg0SGlotQ+9yVCskNBairyHLHuA/CIgc9vBnBaSvn1ga8dBrCpY8PIfBOk5dxmBCnHKmepeT/qWMwFWs1mh1Qeuklkb20xdlhkd0gVM2Wv7cUCWaFT9liiS2bAC4qP7C3XHQjByB6Zb1x/d6l5KiLweFxJtEb2BvrICCEZoSA1FXmuWj4E4KVCiD8E4AK4HsC7djzmamyP9ZGK0gsMR/ZK6pDSWUiZBVvDZLcq0e6FqNkidVh4fjZBqhdGE0XGeMrebOJgay8je1y0kinwwwjLBe6vQOywXWk42HSrvQAk84071CGljse80VRF0h5SDWtvh31khOTH3QDOeWi+nxHskMqzCnwTgJ8F8LLk8+OIJ+8BAIQQRwH8KIC3ats6Mrf4Gu/SDMMS5RSkwkjfYiIL7JDKR7cXXwCou9JuxlLzIJw8SUyPQ0pF9oq7wK+ld+S5aCX58TO8N0yw0qxhs0uHFJlfXD9Ec8SUPfb+VBOddRk1iw4pQnLjbgALa/l+xrKBwDOzPXNC5qsWKeUpIcQPAHhK8qU7pZRbAw9ZRixWfUTj9pE5xXRkz7YEwjJG9pITv13YlD2rlMKeKTq9AEsNB40kYpTFIRVGEpGc/F5wNLjV1BTA5WZxgpTaV0MuWskU9ILJ/WomWF2o0SFF5hY/jBBEcveUvTRCzRsEVUTnzeB+qTn3JUIy424AzbyClANEbTPbMyfkumqRUnYRR/eGfe+rAL6qY6PI/GM6sldWh5RpZ9lOdMTEqkS7F2KhbqPhxBcBXgaHVNa/qW1ZM4s6rT1wSKkOKXaWkGnwQ1l4hxQArDYdTtkjc0s3GaixUB9eau4HPB5XEV/jzeAaS80JyUcYAL0WO6SmYKqrFiHE1QCuAbAspfxLvZtEyoBpYcW2BKISXgAXXWpuM7KXi24vxFLdSWMSbgaHVL9kdEKHlD27Q6rlBrAtkTq4ikAIkUQ/uWgl+dkrh9RKs4b7znYK/72E6EBNeN1Zas7en2qjIns6YtA19kMSkg93I/44lSBV7Q6pXEcsIcSjhBCfR+yEeh+APx/43jEhREcI8TS9m0jmEUb2piMVpIqK7LHUPBdtL8jtkArCbO8FXR1SS3UbQhSz/yjKOmSAmMcPI9SdYvdXAFhdcLDFyB6ZU9xeLDzsjuyx96fKpINxdJSaq32J4iYh2XDX44+5BSm78g6pzGqBEOJKAJ8EcBWAtwH4Hzse8ikADwL4eV0bR+YXv4DIXhnPkUHBU/bYIZWPrh9isW73O6SCyTth5sie0DFlLyx8YhkQ/9tYokumobdHpearzRoje2RuUQM1dpeac8peldGZTqilHVI8txOSiakdUhSk8hyxXg2gDuCHpJQvA/C5wW9KKSWAfwbwOH2bR+YVP42emXJIoZwOqXAPIntlVPYMETuQHDRqqtR8skMqa5+abQlEEpAz7NdtLy5dLxrHFgi5H5Ep8Peq1LzpoOUFpYx+k/LT7SUdUjsdUunUU+7XVaSXrCH1RPbYIUVILmaJ7ElG9rLyFADvl1J+fcxjvgfgwtk2iZQBnTn2YdhWOZ09pqOOO3HYIZWLblJq3qypyJ4+h5SKac6yX7d7QaET9hSOJXgBRKbCDyVqe1BqvrZYh5TAepcuKTJ/qA6p3YIUHVJVRv3daxpi0A47pAjJx0wdUnRIZWUNwH0Znq8+/eaQsqAzxz4MO+nIKdvdbeVWsovqkLItdkjloN0LsZQzspdVZLTV4m+Gv0fLC/YksudYFi+ASG6klOiFe+OQOrLaBAB8f8Mt/HcTMivdUaXmlnK18LxeRfRG9pTbjud2QjKRClJr+X7OsllqnuOxpwBcMeExDwdw7/SbQ8qCztGzw1BPWzYxRS0ia0V1SLGMOhexQ8pB3bYghN7InhaHVBIpLBodEwJJ9VDHuyKnQirOPxALUvdvUpAi88dEhxRFhEqiIns6BuOo56BDipCM0CE1NXlWgR8H8DQhxFXDvimEeBziWN9HdGwYmW9MR/as5EQZlaxHKjDsLNuJbQn2A2TEDyP0wiidYtdwLLh5InsTLrrtdKLNDA4pd486pBj9JFPQv5tf/JQ9JUh9n4IUmUNcP37v7C41Z+9PlfHDCDVbaJm0y32JkJy4G4CwgfpSvp+jIJVLkHo9gADAp4QQv4GkK0oI8fDk878DsAXgD7VvJZk7iorslc3do8SIwiJ7dEhlpqNKZOvxHemGY2dySGV1velwSMWRPXvyAzUTRz+5aCX56LsHi3dIHV5pQAhG9sh8oiJ76nykUOcRRvaqic4hEXWb8U9CcuGux+6ovIKw4JS9zLfSpZT/IYR4BoD3AHh78mUB4MvJx3UAT5dSfk/7VpK5w3xkL7l4L51DquBSc7uc5fAmUFONlAOp4VjZOqQyukDUPj2tsCOljDuu6JAic4K6cVHfg8hezbZwaKnByB6ZS1Rkr+kMn7LH43E1CSKpbf3osCCfkHy4G/njekDikKr2+yzXlYuU8h+EEJcBeA6AHwFwCMAGgH8B8C4p5YP6N5HMI8YjeyUuNReiWIcUu3+y0e7Fdy8WkzvSzZqdXhSMo5c5sjebQ8oLIoSR3Jspe+yQIlPQ01jAOw3nH2gwskfmkpEOKXZIVRqdQyJStx3P7YRkY2pBig6p3FcuUsp1AG9L/iNkKMYjexriTfuRIJKFFZoD8evIu1/ZUA4pVSKb1SGl7O6TxNlZ9+mWF5/M9mrKHnsmSF6yvjdMcf5qE/ed7e7J7yZkFtzkfLRzIECNMatKE0f29Ky7hRCJ+5nndkIyMZNDqtqC1N6sAqdACOEIIV4hhPiWEMITQtwnhHjrjscIIcStQoh7hRBdIcSnhBCP2qttrjJZR91Pi1XayF5UmDsKiGNkdLZko50IPmlkr5ZVkMomzs7aIZVu315M2WMXGZkC1SG1F5E9ADiy2mRkj8wlbhChWbN2lVfXGLOqNL5GhxQQr+F5s4mQjFCQmprMRy0hxK8LIe4SQlw44vsXJd9/nr7N28a7ALwYcWn6DQBeAWDnrc1XAHgVgDcCeBqAFoCPCSHON7RNZARZR91Pi7p4L5sr3Q9lYRP2gNiVQyEhG50dEYmmky2y52eMJfU7pKb7e2y52wWzInFsdkiR/GR9b5ji/NUmznb8TO9jQvYT3V6YunUHcTRMayXzix9KretuxxZ02xGSFXcDWFjL/3OWA0CW76I2B3muXH4RwEkp5Ylh35RSHhdC3AfglwH8qY6NUwghngrgWQAeKaX82ojHNBELUq+XUr49+do/A7gHwAsBvFLnNpHx+GEEx9IzenYYaspe2XoSgkjv3a1JMGqVnY6XlJrX+w4pNXp7HFljSepCYlaH1F5E9mq2hVZQ7bs7JD+9jIX/pjj/QBMAcP+mi6OHco5pJmQPcf0QzSGClHov8bxeTUw4pMq2zibEGFM7pJL3bBQAVl3vNs0JeY5aVwH40oTHfBnA1dNvzkh+DcDHR4lRCT8KYBXAHeoLUso2gL8DcJOBbSJj0DnpYxhWSR1SYSQLjezRIZWdzo5S84ZjwwsMOKSmvBupSteXGrsvUkzD/YhMQ8/w8ItJKEHq+xuM7ZH5ousPd0gJIWBbgoJURfHDSGsEmhN0CclI0AP8zvSRPQCQ1XVr5zlqHQCwPuExmwAOTr85I/lhAN8UQrxdCLEphOgIId6/Iz54NYAQwLd2/OzXYUYkI2PoBZHR6Jm6filbh5QfStQKFKQ4HS07naREti9IWfAyOaSK6ZBSbq1hd81NEzvtuB+RfKj3xl51SJ2/mghS7JEic8YohxRAEaHK+KFM1xI6qNlW6mQlhIzB3Yg/NqeN7KHSPVJ5VoEnATxiwmMeAeCB6TdnJOcDeC6ARyGO7v0qgMcA+BvRz4QdBNCScpe8eBbAohBilwdOCPECIcTnATzm5MmTBja7ugRRZPSutyVKOmUvjOAUGtmjIPX/s/fuYZbkdZnn+4vLOSezqrKyurq7umlAEVAuwrasjzPLCIIiKi4X73cWZR92HGVcGX12mNVZBge84Kyo4+jorCA67qg747gq6ACCCoogF90ZoUW5WdA0dFZmVlWeS9y++0fEL86pzHOJiBMRv7i8n+epJ7syszJ/nRkn4hdvvO/7zcpckIovHCPXxjSTQypbwb+95bju+SCB+uNPrq0Qds2uSCrHdIfUlYXIHiFtYurHpebLiIuoeV3vI17pkT2Km4RkIhWktnBIUZDKxFsAfLlS6guXfVAp9RTE0bg3l7Gw018++fNcEXm9iPwagG8D8AUAvnjh85adNdWqj4nIz4vI5wN49913313ykvuNH1Qb2dPxpqhjDqkgqrvU3EIYCaRjP8cqmHgBlEJ6E5DXIbW5Q2o7kVULQrqLqk5sPpEnBZgPvzAjSF0YOtgd2Pjk8czI9yekKBM/TAdsnCZ2PvMBQR8pPbLHDilCslGKIMXIXhZ+FICHeGrd/6mUeqZS6vHJ258A8EYAs+TzyuYQwP8nIgcL73tbsp7HLXzOBaXU6Sv0PoCxiPgVrIuswA8rjux11iFVrt16E+6WIkifOPFC7Lp2WtQ/dCzMggyCVMaJk/Oi/mK/Cy0I1dlBpnFtCz43rSQnni78NxTZU0rhrr0RHVKkdUz9ECNnVWSPDqm+EoTlPgx2LE7ZIyQT06TVqIggpRZKzXtK5nFMInKfUurrAfwqgP8VwPcsfFgh7o/6ZhF5f7lLBBD3QA2XvF8B0HdBHwBgA3gUgPsWPucxycdIjfiRVBvZ66iQEkRRrQ6XeUxMsGJvSxLGXoidwfyUOXLtTOPitUNqk1CUuv4KO6Tif1enw07jWAohN60kJ77hUnMAuLI3YocUaR0TP8RohUNqYCsE7P3pJfGUvXI7pHgsEZIBRva2ItcuUER+F8BnAfh+AP8RcTzvPwL4PgCPFJHXl77CmN8B8ESl1O0L73sqABfzyX9/glgU+zr9CUqpXQDPBvCGitZFVuAH5ebYT+N0OLJXZweQ/jmyR2ozYy+4ZYKddkhtijtqcXZed7ccx97SIRWZc0g5toLPY4jkJO2Qcuo/ZjV3XRxxyh5pHTM/WjplD9AxK56P+4hXveXMLQAAIABJREFUcg+pa9MhRUgmGNnbiswOKU0Sm/tXFaxlHT8P4B8D+G2l1CsBXEAcDXyTiLwtWddUKfUjAH5QKXWI2BX1EsSi20/XvN7eU3Vkr7MOqVBqFRS0G4vuls2MvVvHbA+T//bCCMM19rJYnN38O7X176JwZM9ch5Rj8SkqyY9nuNQciB1SD1yfIookva4Q0nQmfriy1NyxVSr2kn7hh+UOFHJsi8cSIVnglL2tyC1ImUBEriulvhjATwH4D4i7o34LwPee+tQfQSxAvRTAZQB/DuBLReSBGpdLELtCKi0172iHlF/3lL0tJ7v1idghNT9lDpPem6m/QZAKI7gZOnK2dauZdEjZnNZICqBLzU11SAHA3RdHCCLBwYmHOy4sawYgpHlMTj0gWcS1LA6Z6CnxQKEyI3sK0wzDWwjpPdNjwHIBdyf/v7WSc3mPHVK5doFKKUsp9WKl1DuUUsdKqWDhY5+nlPo3SqnPLn+ZgIj8jYg8S0TOicglEXmBiBye+hwRkVeIyENFZEdEniIi761iPWQ9flDuU5rT2F11SNUc2bMZ2cvM2AuxOzjrkJoF6y8gXiiZXEvzY7rY5k+/Fuo8fjQcDU2KoKMgpjukALDYnLQGEcE0CDFaGdmjQ6qvBFG5dRl0PxOSkelxHNfbUM+xFDqksgtSSqkB4kl6rwbwSAA3EJeKaz4M4DsAfEuZCyTtpPLInnZIdbBDqs7IFTuksjOenRKkElfHbMPTwyCMMMjwWmizQ4qjoUkRvMB8ZO+ui7EgxR4p0hbi7kKsEaQsdvr1FK/k/lbXVuk0VELIGqZHxfqjgAWHFAWpLHw/gKcD+BcArgD4d4sfFJEjAH8E4MtKWx1pLZVH9tKJZJV9CyMEYZQKE3XADqnsjP0Au6em7AGbHVJZI3vbuv7SKXtGOqQY2SP58cMItqWMiKiauxKHFCftkbYwS4Tc4Yrrimtxyl5f8cOyI3t0SBGSCe2QKgIdUrkEqW8B8HYRebmIRACW3X18GMDDS1kZaTVZi5yLorWuzjmkQqnUWXYadkhlZ+KF2FnikNrUrxBvELN0SMWfUzT6pjeNJu7tHcuCSPcitKRayh5RXoTbzw9gKUb2SHvQD0HWRfYYoe4n8Tm13FJzPmwiJANlCFLS33uxPGetRwB4x4bPuQbgtuLLIV2h7Bz7aXRkL+rYhTKI6i01Z4dUdk5mIc4ti+wF6y8gXkbXmzY2FZ6yFwkcS0EVya9viRY22VtC8jArOV5SBMe2cMeFISN7pDXomPhKh5RtwedDpt4hIkkPaYmRPYt9ZIRkYitBipG9PGetCYBNswwfDuCo+HJIV8jqCilKl0vNTUT2+DR1PVEkmPghdgpE9oIwyjRFLI1PFnT9hVG97rpFnI6+Hkm1lD2ivCh37Y0Y2SOtYbZhOmUcs+K5uG+kQyJKnFpKtx0hGWFkbyvynLXeB+CZSbn5GZRSFxH3R72zjIWRduMF1Zaap4JUFyN7BkrNKSSsZ+LHotNSh1RJkb1t3Wp1F+Ivol193LiSPPgZxdqqubI3YmSPtAb9EGTorIjs0dXSS/TvvOwOKR5LhGRgegzsbPLtrICCVC5B6hcAPAzAv1dK7S1+QCm1D+C1AC4B+LnSVkdaSxBV++R7XmrerRvgujtVbB21or1/LWMvvgG4dcpeNoeUl/F3moqDBTd/YSTGyqFddpGRAlTtpM3K3RdHuJ+RPdIS0lJzd41DqmN7I7IZLRyV+WCKghQhGfCnQDAt7pBSjOw5mz8lRkT+b6XUMwB8O4DnADgEAKXUnwN4PIAhgJ8RkddXsVDSLiqP7Kludh/VHbuiQyobYy++SCxO2dM3A5s6pIIwwrnh5lOtbW/rkKp3QuMi7CIjRfAqHn6RlSsXR7gxDTD2bp2kSUgT2dQh5dh0SPURTzukyozscYIuIZuZXY/fbh3ZW/+Au8vkOmuJyAsBfAeAvwJwBwAF4EkA/gbAC0XkxaWvkLQSv+LInqUdUh2L7PlhVHNkj1GrLCxzSOkOqam//gKSfcreduJgEBp0SCXHEW+CSB68MMJgReyoTu7aGwEAi81JK9DCw+rIHjuk+kjaIVXi3tthHxkhm5kk9dmjopE97ZDqryCV+1GgiLwWwGuVUjuII3rHInJS9sJIu/Grjuypbjp7ai81Z9QqE6lDasHplHXKXtYYZjkdUmYdUl17PZJqiUvNzTukUkHq+hSfdcd5w6shZD0zX3dIrYrs0SHVR4K0Q6q8vffAVvDCCCJiZIIvIa1gehy/Zal5YQp700Vkgnjy3i0opQYi4m21KtJ6OGWvGEEkaUF0HTBqlY3lHVLZSs3jDqkcU/YK/i7Cmo+dRbSw6fNJKslBHNkz3yF15WIsSLHYnLQB/RBktKJDyrEZs+ojfgWClN5TmJziS0jjoSC1NaWdtZRSllLqRQA+WNbXJO0kiqTyi1dXI3tBWG8PkI5ahRQS1qIFqR03f2QvyCjO6l97Gx1S+v+PTjuSh6ZM2ZtH9maGV0LIZrQgNbCXR/ZYRN1PvCDeO5TZy+ds2W1JSC+Y6sgeO6SKkmknqJQaKaWepJR6glJqsOTj3wTgAwB+FsA9Ja+RtAw9sa2OUvMu7bmiSBAJan0KRYdUNnRkb7Gc3LEULFVeZE8pBdtSCAuKOmEUGeuQSo8jCpskB15DpuydGzq4MHTokCKtQE92XTtlj+fi3lGFQ4r9kIRkYGuHVPKapUNqNUqplwD4NIB3AXgfgI8qpZ6TfOwxSql3AvgVAI8C8P8iLjknPUbHdqqcnqRfu2GHHFJ1CHmnYYdUNpZF9pRSGDp2enOwCj9jZA9AIkgVW6NvstScT1FJAfyGRPaAOLbHUnPSBjZO2bMUr+k9pJrIHh82EbKRVJAqWmrOyN7aDqlEePrx5K83EU/VuwLg15KP/QaAPQC/B+AHReTdFa6VtAQ/qF5Y0X07UYdugHV3UK2l5h3t4iqb8eysIAXEHR6bHFJ5enKcrRxS5joe5tMaeRNEshNP2WtGL8ndF0f4JB1SpAXoa87KKXu2BT8UFlH3jPnD4BIdUjYdUoRsZHoM2EPAHRX791qQEkb2VvGdACIA3ygieyJyAcDzAQwA/Fbysa8UkWdRjCKaOiN7XXJk6M1EnS6XuZDQnZ9jFcwdUrdq+EPH3twhFUlmt6BtFS+jjTukDJWaM/pJChBP2WuIQ2qPDinSDjzdIbVqyh7Px71k7pAqbw+pv5bPY4mQ1UyPi8f1ADqksFmQehKA3xGRX9fvEJFfAfC7AIYAXigib6hwfaSF1BnZ65JDqoqRvZuwGdnLxNgLMHSsM2LhMINDKk9kL3ZIFZ2yV28h/iJ6Eg+FTZKHJkX27tob4dM3Z3SLksYzC0K4tlr58Irn435SSWSP7mdCNjM9KkmQokNqFbcB+G9L3v9fk7dvLHc5pAvUIazojViXOqTSyF6NsSs6W7Ix9sIzcT0AGDl22uexDBGBn6O42bas4g4pgx1STvoUlZtWkh0vjOA2YMoeEHdIhZHgwZuctEeazSyIVsb1gEVXC8/HfaLKDimf4iYhq9naIZWcz+mQWokNYNnubAYAInKz9BWR1lPFRfE0lupe95HPDqnGcuIFZ+J6QOyQmq4pNdebuKyj7R1LISy48QuMdkglxxE3rSQHXtCcyN5de3H3A2N7pOnMgnBloTmw8KCJ5+Ne4aX7jfL2AQN2SBGymW0FKUVBKstOkFc0kgsvqD6yp50gXYzs1dkDxA6pbExWOKSGjrXWIaWjkFlFxm07pGxjHVLJccQn8iQHfiiZxdqqSQUpFpuThjPzo7WvG+06ZMyqX1SRTmD8k5AMsENqa9ZO2Uv4XqXUt5963z4AKKU+tOTzRUQeufXKSGsJaiw171JkT7tp6nS5sEMqG6sie0PHxthbfQHxU3E2o0PK3mbKXpSW2daNazP6SfLjhVGlDy7ycOXiEADwAAUp0nDiyN4aQSp5QMAi6n6hXUxOFZE97hEJWU1pglR/X2dZBKn95M8yPnPJ+3gF7Dm1RPY66JDSsbk6S37ZIZWN8YrI3si1cO1k9QXE06+FjC4QW23hkDLYIWUzIkJyEkaCMMrer1Y1t58bwrEUI3uk8cSRvdUdUlpEoEOqX3gVDBRy6aInZD0isSC1s0oqyQA7pDYKUo+oZRWkU+jIXtVOH9tSHXNIxZvHOkUFdv9kY+yFuGvPPfP+oWNjtrZDKhGkckT2ooLHdGiwQ8plzwTJiT5WmhLZsyyFK3sjRvZI4/GCCEN3TYdUej7mdb1P+MnE3zJ7+ShuErKBYAqE3pYdUirukaIgtRwR+WhdCyHdQce/qi6rtS2FLl0jg6j67q3TaPGL1v71jL0Qu8MlpeaOhVmwpkMqzOd6sy1V+ElkaLBDymY5PslJKkg1xCEFAFf2hozskcazObLHKH4fqSKdoL+W16XNNiFlMj2O324jSAFxbK/HglRzdoKkM1SRY1+GrYr37TSRMKq/1FwplQh73fk5VsHYC7DrLumQcm1M15Sa543sxR1SxUQdP4pqndC4yLxngoIUyYYXVB/tzsv+7gDXJ/3dEJJ2EAtS6yJ7iUMq4Pm4TwQV1D6k/ZB02xGynMlR/HZrQarfDqnm7ARJZ6hjyh7QPYdUWmpes6jgbDHZrS+MZyF2hyum7GWI7A0yvhZsyyr8uwgNdkjNeyY69IIkleKnI8qbsw0ZOham/urXMyFNIO6QWhfZYxF1H5mL/OXtAzhBl5ANlOqQ6u/+ozk7QdIZ6orsWQqF+3aaSJBO2av3ZelYih1SaxARjP0VU/bc9ZG91C2Y0fXmWMUdUkEkxiaW6WmNjOyRrNQx/CIvI9de+3ompAnM/GitkMsi6n7ih7FLWqkSS821uMljiZDlpILUFqXmQOyQEgpShJRGbZG9LW7em4h+mll3MbVNh9RaZkGEMJLlU/YcG14QQVYIo3oTl3nKnqUKP4mMO6TMOqS4aSVZmVXwNH9bNjkeCWkCGzukWETdS/wwKl3g58ASQjbADqlSoCBFSsevYPTsMro2ZU+7lNyai6ld26Idew0TL75BXeWQArDSVTF3gWR7LWzrkKqzf2wRTuIheWliqfmmIQWENIE4spehQ4oPmnqFH5bvknbYIUXIeqZldUhRkCKkVOq60bCUQtShDZcWhep2uXTNaVY2Y3+NIJXcFMxWFJvnfS1s41YLwsiYQ8pJpzrxOCLZSF8bDeqQGrk2O6RI4/GCKH0Ysgw6pPqJH66PchYhdUjxoSUhy2GHVCmsPHMppd6jlHrRwt+fr5R6Yj3LIm3GDxjZK0JdzrLTOJZi1GoN41n8xGJpZC91SC2/iOSNr25zTMcOKTOClJ7WSKcdyUoTp+xph9SqCC4hTWBTZM9hhLqXxB1S5Z5P04dNPJYIWc70GHB2AGe43ddRFh1SK7gXwF0Lf38tgOdVuhrSCeajZxnZy4MWIuouNbftbgl7ZTNeF9lLHFLTlQ6pfK+FbSJ7YSS1948twmmNJA9eA0vNh64NEd7Ik2YTC1KrI3upQ4oPCHqFHwpcp+zIHjukCFnL9Hh7dxTAyN6ajx0AuL2uhZDuUNeNRvccUnoiW72igmtZFBLWcOKtdkjpp9SbHFJ5IntFjmkRQRAJbEMdUkAiSPFGnmREiz5Niuxtej0TYpogjIdsrHVIUUToJV4FpeYDm247QtYyPSpRkOrv3uPsHdac9wH4NqXUxwHcn7zvXqXU8zd9URF5XRmLI+3ED7QrpGJBSnVLkApSh5SBKXvcuK5kbal5egO7qdQ822vBKSgO6n9iKrIHxDdBPI5IVnS0u1Gl5u7c8XhhZHgxhCxBX2vWCbn6OkARoV/4QVT6+ZQDSwjZAB1SpbBOkPqnAF4P4IcB6Kvac5M/q1DJ51KQ6jFBFMFS1ZdzW5ZC1KHIXipI1exy2aZIuw+cJILUueFZQWqU3sCucEgF+UTGog4pU4X4i7g2jyOSndRJW3LEZBvokCJNRwtS6xxS+gEIHav9Ioik9AfBqbjJazshy5keA7slBMosmw6pZYjIu5VSjwLwBQDuQdwh9VvJH0JW4oVRLT1InXNIpW6amiN7ttWpn2PZTJLI3s7ayN4Kh1SUzwXiFCwG1zceJh1SNiN7JAd1TWPNw6bXMyGm0WKpdvMtw2GHVC/xw6h0h71SKonj81giZCnTY+C2R27/deiQWo2I3ADwZgBQSr0WwPtE5JdqWBdpMX4gtdxkWJZCl66R+ma+bpcLHVLrOZklkb0lNwD6pmBlh1TOSWK2pRAWEHUCQ4X4iziWxdHQJDPNnLKXvJ5XDCkgxDReDocUI3v9wgvK75AC4uOJfWSErKC0yJ5NQSojjwBwVNVCSHcIoqgWl49toVORPX0zX/cNGp9+rWeSxPF2l0b2EkfFpil7GYubnYKxt3RCo9EOqW45Fkm16Mhek0rN9et5ysgeaSjzyF6GKXu8rvcKP4yWDl/ZFsdWFDcJWYYIO6RKIvOZS0Q+qv9bKeUCeAyAfQDHAN4vIn75yyNtxGdkrxChodhVURGkL4y9ALallrr+9E3BqhtYL+fkxDZ3SHHKHslDXvdgHdAhRZqOPjbXTtlLeih5Xe8XfiiVPAx2bYvxT0KW4Y9jEWlnf/uvZTmA9Pd1lmsnqJTaU0r9HGKn1PsAvBXAewEcKaV+TilVwm+EtB2vxshetxxSZiJ7jsUOqXWczELsDmwodfb3knbOrLiB1QJN5sieaq9DirZ+kgf9xL1JDqmhy1Jz0mzmHVLrInt6yh7Px33CD6uJ7PFhEyErmB7HbxnZ25rMDiml1B6AtwN4PIAbAP4YwP0A7gZwL4AXAfhCpdSTReR6BWslLaGuyJ5T0E3SVIIwgmOppcJHldiM7K1l4sWC1DJGaYfUqsheBNtSmUVG27IQFXFIGeofW6Sou4v0E8/QEId1aIF5SocUaSj6WrPuoZ9S8TWHglS/qEqQih828dpOyBkmSZMRI3tbk+fM9VLEYtTPAvgMEXmaiHyTiDwNwGcA+BkAj0s+j/SYuiJ7VkE3SVMJIyl9QkoWHJaar+XEC3BuRS/D/AZ2Ral5mE+cLRqfnJeam+yQsjgammQmLTW3muOQGm0YUkCIabJM2QPoaukj1UX2KG4SspRSHVIUpLLy1QDeISLfJSK3lJuLyLGIvBjAnwL4mjIXSNpHfFGsoUPKUoXcJE3FD8XIzRnLqNcz8ULsrHBIbRoT74VRrt9pUZdRGOmuKnM39y6ddiQHWqy1DLr6TrPp9UyIabJ0SAF0tfSRyiJ77JAiZDllClLKBqL+PgzLc+Z6OOLOqHX8IYCHFV4N6QR+GGFQy5Q9hbBDHVJBFME24pCy6JBawzqHlGNbcCy10lERhJJ5wh6g3Wr5N35BAzqkbDrtSA6qunnahrTUnIIUaSg66rpJkIrdtjyO+4QfRrn2G1lxLE7ZI2QpqSBVRql5vzuk8py5xgDu3PA5dySfR3pMnZG9rjmkTDhc2CG1nnUOKSC+MVjVOZM3smdbCpEg93HdhA4p17Z4HJHMeEEDBSldar4igkuIaVKH1IbIHh1S/cMPqxkoxGs7ISsoPbLX371HnjPXuwB8nVLq0cs+qJR6JICvTz6P9Jiqcuyn6ZpDKqypDP40RXuL+sLJmlJzIL4xWOWQ8nK6QLTDKe9xHTaiQ4rRT5Idr6Zodx5GdEiRhpN2SG2K7PFBU+/wk8E4ZRN3SPHaTsgZtCA13Nv+a/W8QyrzlD0ArwLwXwC8Syn10wDegnjK3l0AngbgxQDOA/jxktdIWoYfRjg/zHNoFcNSCl3abwWhGHG4dG1aYdnEU/ZWH89Dx0qfWp8mb5+anTjkwkiw4QH4Lehohm2wQ4q2fpIHP4w23lTXjWsrKEWHFGkuWizdHNljFL9vVBbZsy2WmhOyjOkR4O4CzmD7r9Vzh1Rm1UBE3qyU+kcAfhLAP0v+aBQAH8B3i8ibyl0iaRt1dYPYVv5oU5PxIzOOAduitX8dYy9Y65AaufZKR0WQO7KX/LuCkT3XYGQv7iLjppVkI47sNafQHACUUrHATIcUaShzQWrDlD1ORusVIlLZQCHXVitrCQjpNdPjcvqjgN53SOWysYjIv1VKvQHAtwH4PAAXARwDeC+AXxGRj5a/RNI2Akb2ChFG1ditNxE7pLjZWMXGyJ5jYbrCUZFXnE0dUjkFQu1wM9khxegnyUMTS82B+EZ/1euZENNo996mPZZrWemDCtJ99EPFKgYKOZaFIOzvjTIhK5keldMfBVCQyvsPRORjAF5RwVpIR/BqKjW3LatbDqlQavm5nYZCwmpEBF4QrS2QXeeoyNuTU7RDKmhCh5SleANEMuOHEQYNi+wBwMilQ4o0l1kQR12VWn+up0OqX2h3clUOKbroCVnC9LhEQarfHVLN2w2S1uOHUSWTPk5jq/zRpiYTVFRIuQl2SK1GH1/ronDrSs39nLEk7XDKG32bO6QMdkhxEg/JwayBU/aA2CFFQYo0FS1IbcKxLfi8rvcGP9APpaqZskdxk5AllC1ISX/d2c3bDZLWU1dkz+qYkBJEYsThYtPavxL9c1m3yYsjeys6pKKCU/ZyHtd6s2hC0NQ4Fp12JDt1PbjIS+x47O+mkDSb2QbHroZT9vqFl/yuK4nssSCfkOWU7pDq796jebtB0nr8uiJ7SiHqUIdUEApcAw4X11Yso16Bn9rg1zik1jgq8kb2UocUO6RIx/FDges0q9QcAIbuaoGZENPMgjCTQ8q1+aCpT+iHUpVE9izGPwlZSqmCVL87pChIkdLxQ6knstc5h1RkRFCwLYVIujWxsCxSh9Sa30vcObMuspfDIWUXc0il0UKjHVK09ZPsNNUhNXJWR3AJMU32yJ5KH6iQ7pNO2q3gnOrY7Ick5Awi7JAqkebtBknr8XOOui+KZXXLIRWXmpvpkAK61cdVFmkUbm1kz8ZsbWQvT4eUlfy7og4pgx1SHROISbV4Te2Qcq2Vr2dCTDPzIwydDJE9OqR6hY7suRUMimCHFCFL8G4CEpUnSKnEIdWh+9o8NG83SFpPnZG9Lt0Ah1G+eFdZ6N9Vl36WZeGnvQxrBKl1DqmiU/YKOqSMdkjxBojkwAujSm6etoWl5qTJzIIw03RKhzGrXpFG9irYA1CQImQJ0+P4bZkOKSAWuXpI5t2gUsqtciGkG4hI7pvwonQtsucbnLIH5J/s1gfmpeZrIntrHFJ5XSCWKjplL/58kx1SLiMiJAd+GGHYRIeUY2HqM7JHmknWyJ7LIupeUWWHFAeWELIELUjt7Jfz9azE+drTYvM8Z66PK6V+VCn1qMpWQ1pP2mVTw42x7j7qCuam7BUr0u4DWhhaG9lzLUxXOqQiDHIUNxefsmfeIWVbCsIuMpKRpkb2Ri4dUqS5eBmn7Dk2HVJ9wq8wskf3MyFLmBzFb8t2SPW0RyrPmcsC8P0A7lNKvVEp9TVKKaeidZGWUuVF8TS21a3pcEEYwTHQAcQOqdVooWedwDp0LPihLBWRgkhy/U7tgqXm+vPriMquQosLdEmRLDR2yp6zOoJLiGkyl5pbFBH6RLpXqeChpnY/S0+7bQhZSlWRPQpSG3kIgG8F8McAvgTArwP4O6XUK5RSj6hicaR9+BVO+jiNpRS6dO9ryiHFDqnVzCN760vNgfjJ9WlyT9nbskPKZGSv6NpJP/GDCAN7s9OjbmJBqkMXFtIpZkGYMbJHh1SfyNJ3WRTXtiDCazsht0BBqlQyn7lExBORXxWRpwF4DIBXA3AAvBTAB5VSr1dKPVcp1TwPPqmNeY69jsgeEHboiU0QipHIlc0OqZX4aWRvTYeUG5/ylrkq4uLmPFP2irnVdIeU6cgeMBelCVlH3tdGXQxdmx1SpLHkmrJHAaE3ZJkIXBS9/+HxRMgCqSDFDqkyKHTmEpG/FpF/AuAezF1TXw7gPwH4mFLqZUqph5S3TNIWqixWPE3XpuwFUT3TCU/jsENqJX6QYcpecnMwXVJs7odRrieWOt7XRoeUfs0HfCpPNiAi8HK+NupilDikGE8hTWQWRBi6GSJ7dEj1ioObHgBgf6f8+VNusi/h8UTIAlqQGu6V8/VSQYoOqdyIiAfgdwH8JoBPAFCIo33/HMCHlVKvVkoNt14laQ1BjeXKVvI9ulKiHERSSxn8abQIxqdfZ9E/k3XHs45PnHZIhZEgEuTrkCrokKrzdbcKp2D/FekfYSQQqefBRV6Grg0ROv1IM5kFYSYh12URda/4+NEESgEP2d8p/WunDikeT4TMmR4Dg/OAXVKdto7sCR1SuVBK/X2l1GsQC1E/AeAcgJ8CcC+A7wBwH4AXI472kZ6gb8qzTIHZFlslN8AdeZIdhGLUIUUh4SxZbPCj5Fg/3TszL/gvMmUv35PIJjik9Np9HkdkA1rsGdQw/CIvqwRmQppAZodUx4a+kPVcPZzgyoVRJefUdGAJHVKEzJkel9cfBfS+QyqXrKeUugDg2wD8LwA+F7Ej6j0AfhbAr4rIJPnUv1RK/TKA3wPwtQC+s7QVk0Yz8eIL1k4NgpS1IKTU8O0qxw8jox1S3GycJcgwuUbfwJ7unSlSMmoXjE+GUXzsKGVSkGJkj2RDDwBopEMqfT1HuDAyvBhCFhAReEG2DinHjqe/iojR6wKph6uHYzz0UvnuKGC+/+HDJkIWmB5VJEj182FYZkFKKfXvAHwDgF0AMwC/DODfiMg7l32+iIRKqbcC+OIS1klawiS5Ka9DkNI371FXHFKmpuzRIbWSIC0LX9MhlZaan3ZI5Z84aW8xZc+kOwpg8SnJjpeKtc27UR6mjsd+bgpJc9Gvm0xT9hauJSb2FaRerh5O8PmfcamSr82HTYQsoXSHVL87pPI4pL4DwN8C+DkArxGRaxn+zVsBvLzAukhL0S6RnUH1T767JKSISLxxzNE3VBbskFqNn8EhlUb2TpWaB+HmCX2ncYrUpGiBAAAgAElEQVRO2TM0oXGR+aaVxxFZT+oebHRkjzdfpFnoYzKLIOWkMStBBkMVaTFBGOH+4ykeemm3kq+v9zDs1SNkgekRsPfQ8r6eoiCVla8Qkd/P88VF5O0A3p5vSaTNaIdUFkv5tliqO4JUlvLsquiSsFc2qUNq7ZS95ZE9r8DEyaKuvyY5pBj9JJtodmRPT82kQ4o0C/3QI0tH5zxmFWEHVKS6zCevTxFGUmFkTz+05LWdkJTpMXDn48v7ej3vkMqzG7yilHriuk9QSn2uUur5W66JtJi5Q6q+yF4XhJR0SpqBG7R5bxE3G6fxgywdUqtKzZPi5hy/06IuoyCKjBw7i1DYJFnxC4i1dbEqgkuIadKhMVkiezYdq33h6mFc31uVQyotNQ94LBGSUlmpeT8fhuXZDb4WwPM2fM5zAbym8GpI65nW2CGVlpp3oENKP3laJ3xUhcvun5X40eYb51F6A7u81DyXQ8ouJurEcU/TDik+RSXZKOIerIuRszyCS4hp8kX2+KCpL8wFqWocUs6C244QAiCKgOl1ClIlUvZu0AbAu9oeM/FqLDVPIntduEamDikjU/bi0wCdLWfJ8ntZ7ZCqr0MqaECHlGuxZ4JkQx8jWW6s62a4QmAmxDRpZC9TqXniauF1vfN8/HACpYC796sZC+qyH5KQW/FuABCWmpdI2bvBzwZwWPLXJC1ikmyYRrVM2YvfdsEhpZ882QYcA47F7p9VzEWl/B1SRSJ78xhqvt9FGEnqrjJFlyK0pFqa3SGlX888H5JmMZ+yt3l/RYdUf7h6OMadF4aVdbfyWCLkFNPj+G0lDql+ClJrS82VUr946l3PU0p95pJPtQE8HMBTAPxuKSsjrWReal79jYaVOqTafwOsnzy5RhxSFBJWoZ1KmabsrXBI5bnpLuyQMjShcZH5VCduWsl65q+N5o2jnzse6ZAizWKWY3+1OGWPdJurh5PK+qOA+R7G47WdkJhKBKl+O6Q2Tdl7wcJ/C4B7kz/LEAB/BuB7t18WaSszP8TItdJ+pyrpkpCi/x9MFFOzQ2o1+ongOrFHj64/3TnjB/lvuose00EUGZ+y5xbsvyL9Q9/YDBoY2Rux1Jw0lLRDys0S2dPXdR7HXefq0RhPevilyr5+ukekuElIzOQofssOqdLYJEg9InmrAHwIwKsB/OSSzwsBHIrISYlrIy1k4oe1xPWAhZv3LkT2UuGDHVJNQj9dXicq2ZaCaytMT5eaFxAZ7RZ3SNnskCIZaXZkb7njkRDTzEvNs0T2OBmtDwRhhPuPpnjof1dNoTmwMP2X4iYhMVU6pISC1BlE5KP6v5VS/wLAWxbfR8hpJl5YS6E50C2HVJCKF/WLCuyQWo0fRnAsBaXW/16Gjr3SIVWsQ6rAlD3D8SeXU/ZIRvwGO6TSUnO/n5tC0lx0jDTPlD1ORus2D9yYIYik4sgeHzYRcgtakNrZL+9rskMqGyLyL6pcCOkGE79GQUp1SJBKp7kZKDVn1GolQUahZ+RaZzpn0p4cJ8+UvWJutSCS1OlmCqdDAjGpllSQaqRDipE90kzmU/Y277E4Ga0fXL02BgA89FKFDik+bCLkVlhqXjorBSml1MOT//y4iIQLf9+IiHxs65WRVjL1o9oie1aHboD1hd5Eya9Nh9RK/DBKN/brGDr2malcXoFSc526yxvZCyPzkT0tpvEpKtlEGtlroENqYFtQig4p0jy0SJrFWehyMlovuHo4AYB6HFKMfxISowWp4V55X5OC1Eo+grio/LEA/nrh75uQDV+XdJhpUmpeB9ohFXWiQyr+fzBRTD3gNJ6VBGE2h9TQOeuQmk9OzP56UErBsRTCnE8i/dB8qTlHQ5OseBm62UyhlEpezzyOSbPwckX2kut6Bx7YkdV8/CgWpO6+OKrse7jpscRzIiEAYkFquDfvfSoDpafs9fNh2Drh6HWIxaXjU38nZCUTP8TOgB1SedH/DyZKftn9s5ogijKVkg9d+8wNbJHIHhAf10UcUqb7eJyCheykfxTpV6uT2PHYz00haS65puzxAUEvuHo4xp0XhpUmE9JrOx9aEhIzPS43rgfMxS06pG5FRF6w7u+ELGPqh7i069byvXRkrwsOqcDglD2XDqmV+KGk47PXMXSsMzewfoHIHhAfA2HO30UQCXYN39ynPRO8ASIbaHKpOQA6pEgjmeUQchmh7gdXDyeV9kcBC247XtsJialEkNKRvX4+DGvmbpC0lokfYlh7qXkt365SfINT9vSTVI83YGcIwowOqSU3sPNYUr7TbFGHlPEOKZsOKZKNtEOqoQ6p0RLHIyGmmQUhHEtluialDik6nztNLEhV1x8FsNaBkDNMjyoUpPrpkGrmbpC0lqlX35Q9Xc3Thcje3CFV/0tS9xbx6ddZ/FAy9dwsu4ENUodUPqHIsa2CU/ZMl5pTkCLZ8A06QrOwrBOOENPM/ChTfxRAV0sfCCPBJ47qcEgx/knILVTqkOqnILVuyt4vFvyaIiIvLPhvScuZ+PUJUlq86URkz6BDCoidChQSzuKHUSYXx9CxzkzlKhrZs1QRh1Rk/ObesRjZI9nwwrjzTKmGClKudWZqJiGmmQVRZge6k07P5XW9qzxwfYogksodUumxxD0iITGVdkj182HYulLzFxT8mgKAglRPmfpRjaXm8dsuCClBwXhXWbi2YmRvCUGUccreEoeUjuzlFYqKTNkLwuY4pHgDRDbhBVFjC82BuNScDinSNGZBmPl1kw4r4fm4s1w9jCfsVe2Q0i56PmwiJGF6DIz2y/2aLDVfySNqWwXpBCKCiR9iVFNRrZU8XY+6IEglAoQpUcG1LVr7l+CHUaYY5WiFQ2pg53eB2JbK3YsWRGK8j8eyFCzVjQgtqZbYedhMdxQAjFwLMzqkSMPwgijThD2AHVJ94OrhGED1ghQQu/e78PCXkK2JQmB2nZG9klk3Ze+jdS4kK0qpewDcB+AcgAsicjN5vwLwUgDfCeB2AO8C8I9F5H2m1to3tENkVJtDSpeat/8imTqkDHRIARSkVhFk7JAaumdLzeNC9Pw33Y6d3yEVNqBDCoh7S3zeAJENZI3CmmLo2Lg+6eemkDSXWVCkQ6r9+yOynI8nDqmH7FcvSLm2RRc9IUAsRgHVCVLST3d2c3eEq3kVgJtL3v9PAfwggB8F8Ozkc96klLqrxrX1Gj32vrZScz1lrxMdUknJr6kOKUfR2r+EIMrmkBo6dnr8a+JC9Pyn2CJT9oIGdEgBgGsphDyOyAa8IMKgJidtEYaOdeb1TIhpYkEq2/7KZRF157l6OMEdF4YY1bDnjntGeSwRgulx/LYyh1Q/9x7rSs0fnvznx0UkXPj7RkTkY1uvbPmangLgywG8ErEwpd8/QixI/bCI/OvkfX8K4CMAvhvAD1SxHnIrk5oFKe0I6UJkzy/YN1QWrm3B48b1DH4oGLkZHFKOhWkQ3eJU8gq6QOIOqbyl5s1wSBUR00j/GHthLTdRRYmn7PF8SJrFLAizO6T0kAmejzvL1aNxLXE9AEmHFI8lQioTpFRybu9pZG/dle0jAD4M4JGn/r7pz4eqWKhSygbw0wBeDuDBUx9+MoA9AL+u3yEiJwB+G8BXVLEecpaJFwtSdd1opJG9Ljik9Bh0U6XmFiN7y9A9UJt4wj0XEUaCX3zbh+f/NogwKOB4s638Ew/9UJrhkGL0k2Tg2omH284NTC9jJSOXpeakecz8/B1SPB93l6uHk8on7Gnia3v799qEbM3kKH5buiClYpdUTwWpdaXmr0M8Me/41N9N8Q8BjAD8DIBvOfWxxwAIAXzw1PvfD+Abql8aAeYOqboEqTSy14EngFqAYGSvWQRhtil7X/65d+FLH3cFr/ov9+Fpn3MHHn3lQjKhrz6HlCkxc5G4/4rHEVnPwckMn33lgullrIQOKdJEZkGEvR030+cqpWLHKq/rnSSMBJ84muBZT7i7lu/n2oriJiFAdQ4pAFA2BanTiMgL1v29TpRSlwH8EIBvFRF/ydSqSwBuipxpAjsEsKuUGoiIt+TrvgjAiwB8zv3331/ByvvFNJlKtFNzqXnUBYdUZL7UnJG9s/hRlEnoUUrhlV/1BDzzJ/4Q/+Q3/gL/6TufnET2ijik2tsh5Vh8iko203SH1NA92wlHiGlmQZjJsatxLIoIXeVTN6bwQ6kvsscOKUJiqhSkLKe3HVLmH6ln4xUA/kxEXr/mc5bdBak1H4OI/LyIfD6Ad999dz1PGbqM3sCPaiqrddIpe7V8u0rRkT1TPUCMWi0nCAVuxt/JHReG+JfPewL+8uoxfvatfws/KNYhZVttnrKnuGklawkjwdHEx+UmC1KJQ0o68LCDdAcvyB7ZAxiz6jJXkwl7dUX2YnGTxxIhFKSqYV1kbyVKqYcB+DwAFxFH+t4rIn9X5sIWvtfjAXwHgKcqpfaTd+sz8EWlVIjYCXVBKWWfckntAxiLiF/F2sit6A6puhxSVipItf8GOHVImYrs2Sp1uJE5QZjNIaX5yifejd/7bw/BT/3BB/GQ/R1czBivWKRIzCKImtEhxVJzsonDsQcRNNohNXJtiMTdbAPH/OuKEEBP2csjSPEBQVe5ejgGANyzX49DyrUtTmwkBEgEKQUM98r/2lZ/I3u5Ht8rpR6tlHoj4oLz3wTw2uTtR5RSb1RKfXbpKwQeDcAF8KeIhadDxD1SAHAVcdH5BwDYAB516t8+JvkYqYFpUPOUPdUlh1TscFkSR60FbjaW40eSWyR8+XMej/3dAT56MC4kEjmWyhVDjSKBSFyGbhrX4nFE1nPtJE7P33Z+aHglq9E3/Sw2J00iFqSy768cOqQ6y8dTh1RdkT0+bCIEQCxIjfaAKvbcPS41z/zTVEo9CsCfAPgSxJP0Xgfgx5K3H0re/7bk88rkbQCefurPjyYfexaAVyXrug7g6xbWuwvg2QDeUPJ6yArqnrKnzwVdmLLnR5HRyFXcIdX+n2PZBGGUjs/OyqVzA/zwVz0BAApH9vJs/PxIT2g07+RwbJbokvUc3IwFqaZH9gDQNUoaxcwP8zmkLMUHBB3l6uEEt58f1rbfdm0LHgc9EJIIUhXE9YBeO6TyRPZ+GMBlAN8D4GdEJD0zKaUsAC8G8BMAXgng68taoIg8COCti+9TSn1m8p9/LCI3k/f9CIAfVEodInZFvQSx4PbTZa2FrGda85Q97ZCKOvDUJk9XURUM2CG1FD+UQqLSMx53Bd/zJY/Gpd38kb28U/b05zYhsucwskc2kDqkGi1IxdcwOqRIk5jl7JCKi6h5Pu4iVw8ntbmjANY6EJJSqSDFDqksfAmA14vIGYEnEad+Uin1TADPKGtxOfkRxALUSxELZ38O4EtF5AFD6+kdE7/eDik77ZBq/4YrjCRXV1HZOBzpuxS/4KQ8APjeLy2WYLYtK5fLSN9wNKPUnJN4yHquncwANNwh5erIHo9l0gyCMEIQSc7IHq/rXeXq4Rife09FN8VLcCwLQdhP5wYhtzA9Akb7mz+vCD12SOW5Ax4AeN+Gz3kf4r6nShGR14qI0u6o5H0iIq8QkYeKyI6IPEVE3lv1Wsgc/fSkril7utQ8T99OU9lG+CiDuEOq/T/HsgkiqT0Kl9shFTbMIcXjiKzhIHFIXWqyIJXc9GvXLyGm8RJhKV9kj9f1LhJFgk8cTWubsAfEDin2kRGC6h1S0s99Rx7l4C9wtjT8NI8C8JfFl0PazMQPMbCt2pw+81Lz9l8kdam5KeIOKT5JXUREYudazWXhds7JSKlDyqDDTsPiU7KJayce9kZOoShsXdAhRZqG7u8Z5BCk6JDqJtfGHrwwwl179Q2GcFnrQEhM5ZE9OqQ28UoAX62U+oplH1RKfSWArwLwijIWRtrHxAtz9RtsSxrZ64BDKjAgfCzicuN6Bv00sG7nmq3yOaS0eNUMhxSn7JH1XDvxcLnBE/aAhSl77EwhDUGLo7mn7PEBQefQPXx1nkfZR0ZIAjukKmFlh5RS6vlL3v0GAL+jlHozgD8C8ACAKwC+CMAXA/htALdXsE7SAmZBiJ2aCs2BBUGqAzbiIDIf2fPpBriFVOip2cmRtxg8aFBkz6VDimzg2onX6EJzYD6Yg6XmpClocTRPZG9gc8peFzExqdS1+NCSEIQB4N2sTpBSVm8dUutKzV8L4PSdhb7jeQaWl5c/B8CzAbxu65WR1jHxwtoKzYGFyF4XHFINiOzxSeqt+IaEHrvolD2DgqbGZocU2cC1Ew8Pu62+7pMi6Jt+TpUiTUGLo7mm7LFDqpMc6MEQtTqkeG0nBLPr8dvKSs37G9lbJ0h9e22rIJ1g4ocY5bCTb0taat4BISUuNTcX2RskkT0RgVLmhY0moJ8G5unsKAPHzhvZ01P2zHfyxBER3sST1RyceLj3YRVt5kpCx6LokCJNoVhkT2HKY7hz6MhenU7TgcOeUUIwPYrfskOqdFYKUiLyS3UuhLSfiR9hVKNDCkjcJB1wSIUGprkt4tgWRMyvo0nMo3A1l5oXdUg1IbKXc+2kX4gIDlsQ2Us7pBhjJg0hdUjlmbLH6bmdREf2Lu1WPtQ8ZejYmHHqKOk70+P47Wivmq/f4w4p84/USWeY+iF2aiw1B3QBdK3fshJ846Xm8ffmWN852iFVt0DnWPnKQ3XXlcnIp8ZmRISs4fokQBBJ4wWpeYdUBy4upBMU6ZBy2PvTSQ5OZri069babzlyLZ4PCUkFqaoiezYFKUK2JRak6nVIWRYQdcAhFYSRUYeLLlRn3GqOFoVqn7KX02UUGJoGuAxOayTrmHefNFuQ0j09dASQpjArECF3ORmtk5gYDDF0bASRsCSf9JsJI3tVsa5D6gxKqXMA/hGALwNwD4BljXoiIo8sYW2kZUy8EKP9miN7qhsRocBwVE5vcjlpb47eeNXtXMv7VLtJHVJ5xTTSL+bdJ/WV8RaBkT3SNOYOqXwdUnxA0D0Obnq4XPM5VJ8TvTCqffIwIY0hdUhVJUjZFKQ2oZTaB/A2AI8DcB3AHoBjAAMAO8mnfQKAX/IaSUuYGHFIdeMGOAgjnBvm0odLRYsujOzN8Q05j4ZObI3PWjDfqA4p2+INEFnJgRakdpvtkBrYFpSiQ4o0B07ZI5qDEw+PvvN8rd9zcfJow0/fhFRH5YJUfx1SeWTuH0AsRr0QwKXkfT8B4DyAJwN4D4C/BfDYMhdI2sPUUKl5JyJ7kRjtAEojexQTUnQ3U90OqWEi6madaNOkDinHUoyIkJWkDqmGR/aUUqkwTEgTmE/Zy349Gjh0SHURI5E9l5NHCcH0GFAWMKhIEGapeSaeA+CPROQ1InMFQGLeAeBZAB4D4H8veY2kJUz9EKMcdvIy6MoNsB+aLTVPI3vcvKbon4Wb4wagDPLGhZrkkLLtbrweSTVoQepyw0vNgTgaNaVDijSEuSCVI7KXc0AGaT5hJDgce7WfQ0dprx73iKTHTI+B4V5cYFwFlg1IP/cdeX6iD0PsgtJEWOiQEpFPAXgDgG8sZ2mkbUz8EDuDem/eLaUQdWDDFUaR0VJqTtk7SxrZq1noSZ9EZtz46UhGExxSrmWx9JSs5OCmh92BnU6xazJ0SJEmoeOjeSJ7Q8eiqNoxDsceRIDL5+vukOLkUUIwPQZ2KpqwByyP7P3FrwF/86bqvmdDyKMejAEsXtmOAdx16nMeQFx2TnqGH0YII6m9Q6orJcpBKEaLIrW7hg6pOVroqfv3MndIZbuRmE8DNF806tgKkaATIjEpn2sns9qjJkUZcsw5aRBFInsXd1yMvRAej+POMB8MUfeUvXz7EkI6yfS4uv4o4KwgdeMB4PXfB7zjZ6v7ng0hzx3M3yF2SWn+CsBTlVKLCsQXAvhkGQsj7WKSPIWr+8m3pRTCDnRI+VFkNHLlMrJ3Bl93SBkoNQfiTrYshA3rkALAmAhZysFJ/VGToowcO/PN1+GJhz/70EHFKyJ9RotKgxwPHvaT19rRxKtkTaR+Dm6aiT1rh1TWfQkhnaRqQUrZt3ZIvellQDAFvuLHqvueDSGPIPWHAL5Izcc+/RqARwL4XaXUdymlfgPA3wfw+pLXSFrA1DMjSNlWRyJ7oRgVpAaM7J0hSCN7dTuk8pWHBg3qkNJuMl20TsgiJsp4izJ0rcw3X7/yjo/iW/+vP6MThVTGLIgwcKxMk1c1l3ZdAMDRmMOvu8LByQyAgcieS4cUIdU7pOy5Q+pjfwb8xa8C/8N3A5cfWd33bAh55sz/EoABgIcidkv9HIAvBvA8AM9MPuftiKfxkZ6hHVJGInsd0FD8yGxkb94hxRsqje5CqtshlZaH5iw1p0OKNJ1rJx4ec9ee6WVkYpjDIXU08eGHgutTH7fXfKNI+sEsCHPF9QDg0m4s/h6e0CHVFUxF9vTAIpaak14zPaonsheFcVRv7x7gqd9X3fdrEJkFKRF5D4DvXPh7AOCrlVL/PYBHAfgIgHeJCM9WPUQ/Sd4Z1B3Z60ZfTRCajexp0cWjIJXiG+pmGubc+KVdVwanNGpSQaoLKjEpFRGJI3vnW+KQcqzMr8Fx4hA+nlCQItUwC6JcE/YAYD9xSB3SIdUZdGRPu9/qYpjzQRkhnWR6DIyqLjUPgXe/BvjkXwJf+xpgcK6679cg8jikliIi7wbw7hLWQlqMUYdUJwQpqd2Js4iO7FFImOMnG6+6px/mtcYHhrqulpFG9ihsklPocuW2RPZGro0b02DzJwIYe/HnHU9440+qYeZHhR1SR2M6pLrCwckMl3bdxg9bIaRzBB7gj6t3SPlj4M0/BHzmU4DHf1V136thFBKklFIugMcCuIh42t77RYQ7sR4z8fKPJC4D27I6EQ8KIjE6JY2RvbPMhR4zG7+s/TWN6pBiZI+swFTUpChDx8LUz3bzdTKbO6QIqYJZEObeX9Eh1T1M9fCx1Jz0ntn1+G3VHVLBFAh94FmvAnJ0BradXFc3pdRlpdQvADgC8F4Ab03eHimlfkEpdXv5SyRtYBqYckgBUQem7AWmp+wl7hoKUnP8tNS87il7+UrNG9UhRacdWcHBiZnpUEUZOlbmeMrEjx1S1ylIkYooEtnbcW0MHItT9jrEwU0Pl8/VHwumQ4r0nulx/LbSyF5yjv97/xC487HVfZ8GktkhpZS6gri0/LMQu6LeCeCTAO4CcC+AFwJ4ulLqH4jIAxWslTQYPWWv7g4pW7U/sici8A1P2dMOKU6JmjMvNa/XIZW31LxJHVKpsMkpe+QU15LpUO1xSGUvNadDilSNl0zZy4NSCpd2XRyd8LjsCgcnHh595/nav6+eoM0OKdJbpkfx2yodUnc8Brj9s4Gn/W/VfY+Gkufq9krEYtSrAXyGiDxdRL5JRJ4O4DMA/GTy8VeUv0zSdHSH1CjnE7xtsSzVeoeU1tOaMGWPUas5aRSu7g4pJ9/GL3VINaBDSru02i4Sk/LRZbwmnu4XYeTmcEjpUnNGo0hFFJmyB8Q9UofskOoMpiJ7WgzllD3SW1KHVIWC1L3fDHzXO6v9Hg0lT4fU/wjgj0XkJac/ICLXAXyvUurzATy7rMWR9pCWmtMhlRs/NF9KzcjeWeaRvZo7pLRDKmN/jXYjNaNDil1kZDlph1Rbpuy5dvYOKZaak4qZBRHOD/PXvu7vujiiUNoJwkhwOPaMxJ5tS8G1VVrPQUjvqEOQAnrVG7VInjutCwDetuFz/hhA/V5SYhxddDiquUPK6sCUvSaUUjuM7J1BR/Zqn7Ln5IvshaH540ejf1bskCKnuXbiYeBYOFfzQ4ui6A4pyeDAHScOqetT3viTaoin7OV/7ezv0CHVFQ7HHkSAy+fNuEyHjk2HFOkvdQlSPSWPIPUBAHdv+Jy7AdxXfDmkregnybWXmqv2R/bCBnQADdIpe+3+WZaJdvnUXRY+sC0old0hFTSo1NzmlD2ygoOT+Mm+asnTv6FjQSTbOXFMhxSpmCJT9gDg0jkXRzwuO4HpSaVxjJkOKdJTJjV0SPWYPFe3nwTwDUqpJy77oFLqXgBfj7hjivSMiRfCUvW7SewOOKR05Krun90ic2cLn35p/Ejg2qr2G2ilVK4JX2EksK3617mMtIuMxxE5hanuk6Jot++miEoYSeoQpiBFqiKespdfkNrfHeBo7GVy+pFmM+/hM3MejQc98NpOesr0GFA2MDhneiWdZGUgXSn11FPv+jCANwJ4p1LqdQD+CMADAK4A+CIA3wbgDQA+UslKSaOZ+iF2XLv2m2LLUmi7qSedkmaw1DwWNNj9s0gQRsZca3k2fkEiSDUBh6XmZAUHLROkhoslvqPVnzdZcDIeT4Kql1U6Uz/El7/6j3Dp3ADPeOwVfOnjruDRd55vhMBN5sSCVH4H+qVdF34oOPHCQh1UpDkcJJNKzUX2sj8oI6RzTI9jdxSvjZWw7ur0VgDL7ioUgP8ZwAtPvQ8AngvgOQDaURJBSmPih7UXmgPxDXDY8hHzpqJhiyil4FoWvLareyXih2KsaD7e+GWzxodR1Ij+KGBezO9TkCKnuHYywyMu75peRmbm0y7Xvw7Hs7kIdb2FDqkHb87wkYMxbs5CvOr378Orfv8+POy2HTzrCXfj+5/5OUYflJA53hYOKQA4PPEoSLUc05G9gWNlHvRASOeYHgM7+6ZX0VnWXZ1ejuWCFCFnmPhh7YXmAGAphbaberSbxGRkT39/Rq3mBFGURtDqZuhaaQxoE34ozRGkLEb2yHKu3fRwqU0OKTfbcIGTpND80q7bysjeJFn///Hsx+ELHnEb3vz+T+E333sV//YPP4Tn3XsPHnv3nuEVEiDpkCoiSO24AICjsY+H3Vb2qkid6MjepV3XyPcfuozskR6jHVKkElYKUiLyshrXQVrO1JAgZVtA1HI3RpA4vEyWmgOA61iM7C0QGBR64sheVoeUNMbFoB1SLDUni0z9ECdeaKz7pAjaIbXJEUge95QAACAASURBVKALze++uIO/uv96HPVtyOsxCzpyuDuwcWVvhG/+ew/HI+84h2/4+XekN8DELCJSuENKi8CctNd+Dk5muLTrGju/jBwr87AVQjoHBalKac+uiTSaqR/VPmEPSErNW17W6adT9kw7pBjZW8QPxZhDauRamccrN6tDSjukeByROfOoiZnukyJkdUiNE4fR3Rfjoqnr03b1SOn1L16/dUeN7qwhZvFDgUjsUMmLdtNw0l77MT0Ygg4p0msoSFVKoUC5UuoLAXwegH0AxwDeIyJvK3NhpF1MvNCIIGUp1XqHlI7smX6q7lqKDqkF/DAyFqPMU2rexA6poOW9bqRcTHefFOGWUvM1pILUfixIHU/8Vv1/6sjeYgekdrLRIdUMtFt2mw6pIzqkWs/BTQ+XDYr6LDUnvYaCVKXkEqSUUk8C8CsAPke/C0nPlFLqPgDPF5E/L3WFpBVM/BAXRvUXZnbDIZVE9kx3SDkWu38WCCJz0Zs8peZNcki5iUPKp0OKLHCQCFKXz7dHqNER9Kyl5ndf3AGA1vVIzSN78+v3xR0XtqXokGoIWgTYpkPq8KRdxyU5y8GJh0ffed7Y9x8yskf6DAWpSsmsICilHgXgDwDsAXhb8t/3A7gbwNMBPAXAG5VSXyAiH6xgraTBTP0Qd16o/8mNrVTrR8zrvh3XdIeUbVFIWMBkWfjQsXAjY/QnMBgtPM0guWHy+BSVLHDYYofUpuECJ6cie20TpJZF9ixL4dLuIHW2EbPMBan8LnTHtnBh5LBDqgMYj+zlcG4T0imCGRBMKEhVSB5Lyw8COA/gG0TkN0597GVKqa8F8B8A/ACA/6mk9ZGWMPXDWyz/dWFZ7Y/saYeUaZdL3CHFzYYmCA1O2ctZam762NEMU0GKT1HJnNQh1SpBKptDauK13CGVrP/09fv28wM8yMheI9CuFN1rlpf9XZeRvZYTRoLDsWf0HDpyszu3CekU0+P47Wjf7Do6TJ6r2zMA/OclYhQAQET+HwC/lXwe6RkT30yHlK3aH9nTDi9TfUUa12aH1CJBJMZilPHGL2upeXM6pLIWQZN+ce1kBttS2BuZGVdehLRDasOx3HaH1OKUvUUunx/g4CYje01APygaFHxAcml3wFLzlnM49iAyHzhggqFjZx62QkinoCBVOXmubrcD+MCGz/lA8nmkZ0y8MO3cqBPb7kBkL2xIqbltcTraAn4YGYtR5tn4NckhpW+YKEiRRa6deLi0O4DVkOM0C/MOqQ2l5rMASgF36Sl7Lbvx15G909fv284NGdlrCPpaUNwhNcDhuF3HJbmVJgyGGOZ4UEZIp0gFKUb2qiLP1e3TAB634XMeA+DB4sshbWUaRGYEqQ50SKWl5sYje4qRvQWCUOA6hjqkcljjg8hc19VpHNuCYyna+sktxNOh2hPXAxbcfhtKfMfJhNmRa2PoWO1zSHkhho51RtS+fG7AKXsNYZsOKQC4xMhe69GvRZODIYZOXOvQ9j33NnhBhJ9+8wcxZbl7v5gexW8pSFVGHkHqDwA8Ryn1jcs+qJT6GgDPBfCmMhZG2kMYCbwgMhPZs9ovSOn1G5+yZ1uM7C3ghxEcYw4pa2OZsiYIm+OQAvQkHh5HZI7pMt4i5Ins6Ql1F3dcHLfMiTLxwzNxPSAWpG7MAorLDUD/DopM2QPiyN4h3W6tRk+8vHzObGQP6PfQkvd87BD/6o1/jT/5W3ovegUdUpWTp9T85YgFp3+vlPouAG9BPGXvLgBPA/CFAG4A+Jclr5E0HP2kYFTQTr4NllJouR4FXwtSjZiy19+Nxmn8UIz1eulScxGBUuvXEESR8bjnIkOXk3jIrVw78fDYh+yZXkYuBrYFpTY7pCZekAo6F3fc1jmktMPrNLqr5tqJlxa2EzOkkb2CDqmLOy6uTwMEYbOuFSQ7TYjsjdKOSDNDjJrAOBkCkXUKMukIFKQqJ/OVSUT+BnFh+V8D+AeIp+n9a8TT956SvP+ZIvLBCtZJGowuRTVxgbIttN4hFSQiUBNKzdkhNScuCzezeR+5FiKJ43ibCBsU2QPiG/mmuSruP57gCS/7ffzVJ66bXkovOThpX2RPKRW7/TI5pNorSE1WTMjV0SDG9syTRvYKPvS7tBsPE2jbsUnmHNz0oNT8d2mC+eTR/j5w0p17J7Nm7XFIxVCQqpw8DimIyLsAPFYp9WQATwJwEcAxgPeKyNsrWB9pAXOHVP2ClGW1f8qeFoFMx65c22KH1AJBaG7K3uLGz93wRDuIBCO3OYJUE4tPP3Ywxo1pgPfffx2Pa5lTp+34YYTjid+6yB4Qvw43dYWMvQDnhvPI3v3H0zqWVhoTb4Uglfy+Dhj1Ms7Wkb3kd3k08Y1OaSPFOTiZYX/HNepwS2PMPY7kjxMh6uaM4m6vmB4Dlgu4dAtXRWZBSin1VADXReR9IvInAP6kumWRNqE37EY6pJRC1HaHVLL+TcJD1QwY2bsFP9osBlXFYqHy+eH603TTHFJN7JDSLk7dw0Hq4zApU26bQwpAJofU2Ju/Ri/uuPjAJ2/UsbTSGHsBdt2z5xgtXBzc5GvGNLqzZ1BQkNrfTQQpFpu3lib08Ol9ybRhDug60ZG9m3RI9YvJUeyO2lChQYqT5+r2FgAvqmohpL1MvHizZGTKXhccUlEzpuw5toIftPtnWSZBaE7o0U8ipxmcRnGpeXN6QXT/VZOYJDb7Bxk/qp1590n7nBlZ3H7j2Tyyt7fj4nrLYlETP1rqkNI3v9fokDJOGVP2AODwpF3HJplzcNMz7m4baed2wx441clJspe4yQ6pfjE9Bnb2Ta+i0+S5i3kQwKSqhZD2op+WmJqyJ4JWu6T8sDml5locI0mpecEn0tuSRvYyjBYOosh4/9giejR0kxinghTdHnVz7ab5Mt6ijDKIq2M/uGXK3o1Z0Kpew4kXLL12740cuLaiiNsAypiyB8zdiqR9NKGHb7hQat5XJmmHFAWpXjE9Zn9UxeS5ur0VwJMrWgdpMfoEvTOo/+bdTuyTbXZJhdohZbzU3Or1ON/T+GEE15BDaj7NJoNDKhLj/WOLDN0GR/Z4c107uoNIl2S3iaFrYbrhWF50SF3ciZ0obXJJjRdK2RdRSuHyuSGuMeZqnPmUvWJ7rIuJQ+po3J7jktxKIyJ7LDXHSRrZoyDVKyhIVU6eq9sPAPgcpdQPKaXMjXkgjWNiuNQcaPekvdQhZVyQUulaCIyOyM6z8Wteh5TduA3rhA4pYxwl4sy+welQRckSPz05VWoOtGua2XTFlD0gdrVRxDXPLIhgW6rw9ejC0IFjKRxN+LtsI2EkOBybj+ylpeZ0SOEGBal+QUGqcvJM2XspgP8K4J8BeKFS6i8AfBLA6TtYEZEXlrQ+0gJMTtnTzpCoxQ4pPWXPZWSvUfiRySl781LzTTSvQ8pq3IaVDilz3JjG4szeqI2C1Hq3XxgJpn6URt7aKEiNvXBl3P7y+QGn7DWAWRAWdkcBsdttf9fFIR1SreRw7EHE/GCItNS8YQ7oOjlhZK+fUJCqnDyC1AsW/vuu5M8yBAAFqR5hesoe0G6HVBBFUGru9jKFa1vwQ4GIQHGSBIIwMiYSzqfZtNEhtbkIum50h9TByYzHd83cmAZwbbXVDbUpRq6NG2vKa7XQeW6YCFK77RKkRAQTf3lkD4hvgD9ycFLzqshpZkG09etnf3fAKXstZT4YwqwglZaaN+yBU51MdGSPpeb9goJU5eQRpB5R2SpIq0k7pAxG9tps7AkiMe6OAuYjpf1QMHD6fcMeRYJIzMUo85aa240qNbeb1yGVbCL9UHB9EqTCAameG1MfF0ZuK0XAoWOlD1yWoUeAL5aaA+0RpGZBBBFgZ7B8K3j5/JCuwgbgBVHhCXuaS7sup+y1FP0aNN3Dl5aaN+z6Xicns2TKHh1S/cGfAuGMglTFZBakROSjVS6EtJdJcnEyEtlL7nHaXGoedxWZv1nTLhs/jFJxqq/4icLpGuqQyltqbqp8fRmDBkf2AODBkxkFqRq5MQ1wYZTn2Vdz2OT2Gyc3J6dLzdsiSI3Th0nLz3O3nRtg7IWYeKt7pkj1zILtr8kXdwa4ejguaUWkTg6SwQKXz5nukGKp+dinINU7psfxWwpSlZLpCqeUerhS6muUUl+tlHpY1Ysi7UI/QTYRybA7UmrehClpWnwJWGw+7/Uy7ZDKEtlrZIdUszas+sYbYI9U3dyYBjg/bKsgtb7U/KTlDqnTDq/T3J44Mg44ac8o23ZIAbFDilP22klTInssNQfGiRB1MgsgLX4QTnIwPYrfjvbNrqPjbLzCKaV+HMCHAPw6gN8A8GGl1KuqXhhpD1M/xMi1jHQgWR0oNQ8jMebEWcRNNhte2CwxwQR+8jNwTHVI5dj4BQbL15cxdJsnSE28MBUXOWmvXuLIXjsFqZFrrS3w1XF17ZAauTYGjoXrLRGk0v7HlR1SsSPjGovNjTLzozQuVZRL5wacstdSDm56UCoWFU2i9yV9LjXXD7eCSBq3zyEVkTqkKEhVydornFLqmwG8BIAC8AEA9yX//RKl1DdVvzzSBib+6ik9VaNjZkGLHVJBFDWilHpgzyN7fcdviEMqy8YvjJrhsNMMHRthJAgadBxN/BAP2d8BABxQkKqVOLLXzojk0N3kkLq11ByIXVLtcUit73+8TTuk6Co0yqyEDqn9XRdTP1rbiUaaycHJDPs7LhzDDy4d24JjqX47pLwAeru1buAF6RCM7NXCprPbCwEEAJ4hIo8XkccB+DIAEThJjyRMvNBIfxQAWEqXmrdXkPLDZjiktBuIglQsEgIwtgFMy0MzOaSaIWhqhg102o29EPfs70Ap4EHeXNdKFzqkVkUzdHxjMfK2N3JwfdoOQeq0w+s0tycOqQM6pIxSTmQvFhcPOWmvdVw78YzH9TRDx+p3qbkX4vbz8XnxhD1S/YCCVC1susI9EcB/FpG36HeIyJsA/BaAe6tcGGkP0yAy5pDqQodUEEaNcLi4C1P2+o7ukDIl9KSRvQ0bv3QaYMM6pIBmTeKZ+iHODx1c2h2wD6dmrk997LXVIeVYEFl9ThwvEXRa5ZBK3DKjFYLU3CHF14xJxiU89NtP+s04aa99HNz0jBeaa2LXaHOu7XUShBG8IMKde/HvgsXmPSHtkKIgVSWb7mIuIY7pneYDABimJADMOqRSQarFHVJN6QBiZG+O/hmYcq4ppZJpdet/Fzqq2oTjRzN0mzeJZ+yF2B3YuHxugAdv5HMI+OFqhwxZTxQJbs7a65DS17XpCqfislLwNglSmxxS5wY2ho5Fh5Rhrp14uLylQ2Y/cUgd0SHVOo4nfmMmww4bOEW3LrSAf+eFEQAKUr2BDqla2HS3ZQFYtrPyEXdJEYKpb24kdBcie0EocBvgcNHiCwWpZgg9WTZ+2hnYBIedpomTeMZeiJ2Bg8vn8zmkRARf9GNvwS+/46MVrq67nHgBRNBaQWqT26/tDqlUkHKX/36UUrj9/JAdUgYRkdghc347h8ylc7GgcdSSY5PMGXshzhnaY59m6Kwf9NBl9PnyzguJQ4odUv1gegzYQ8AdmV5Jp8lyF9zeO31SC5Nkyp4JuuGQakZkz6EglWLaIQXE5eCbNn5p11UDjh+NLt9tkkNqmgxeyHtzfTT28YnjKT706ZMKV9dddOlra0vN02N5ubh6sqQU/OKOi+NxO27655G91ee5284x5mqS69MAXhjh9vPbOaTYIWWe999/vZCr5mQW4NywGaL+aMOghy6jO6O0IHXiUZDqBdNjuqNqIMvd1suUUuHiHwD/HABOvz/5w1doz5ganLKnHVKt7pCKxNg0t0XcNLLX3p9lWZiesgd0wCHVkKeoIoKxF2B3EAtSn87Rh/PJ61MAaE1JddOYC1LNuJnKy3y4wAqH1Cw+rqyF19/FHRc3ZkErXLuTJZHD01w+P8A1RvaM8WByvrp9S4fUfhL5OmqJWNo1ZkGI5/7M2/HLf5rfbXtzFuB8QwSpYYYqga6iHbF37MVOGU7Z6wkUpGohiyClcv4xnz0itRI7pMx2SEUtvj4GoRgf5wsAAzqkUoJQO4/M/V5G7uaNnxbOmnD8aAYNi+x5YYRIgJ2kQ+rGNMi8tgcSQYobz2LcSIS8tjukpv6KDik/PNO/tLfjQqQdx8zEi88v6x4o3XZuwMieQR68UY4gNXRs7A5sHFJcNMKnrs/gBRE+fSOf2zAII8yCqDEOqaFjN+ZhU92MT0X2OGWvJ0yOgB3WZlfN2rsYEbGK/Klr8aQZTI2WmsdvgxYrUn4YNSJyxQ6pOXOhx6RDavPGTzukmnD8aOYdUs04jiYLsSrdw5LV8fGp6/HNww06pArRD4fUrf9vF5NpZm3okRr7AQaOtdZhefv5IQ5OZiz2N4QulL/9wnaRPSCetHdIh5QR9MONm7N8P/+T2frBA3UzdHtcap44Sm8/P4ClWGreG+iQqgWKR2RrpkFkLLJnJw6WqMWb5bAhU/b0GrygvT/LstACp9EOqQwbP73ORkX23PW9O3WzWDyte1iyTtqjQ2o7dNRxr62CVIZS89M3im0SpCZL1n+a284NMPWj9HVE6kVH9i6f284hBcST9jhlzwwPpA838l1LdE9RkyJ7fS01n+8lHJwbOtwX9AUKUrVAQYpszcQzN2XPTjukjHz7UvAjMRoN0+jIXpvdZmURhOadR0PHarVDymuKQyqJW+0M5g6pBzOWNH8yfarNjWcR9M+t7ZG9VeJqJwSpDQ+TLp+LRVzG9szw4E0PSsXC4LZcOudyyp4hPlnw4YaOhTUmstfjUnMtSJ0bODg/dBjZ6wsUpGrB/F0waTUiYrRDSus4rS41D6OGlJozsqdpypS9zQ6pBpeaN0WQWojsaYdU1pvrok+1SUzbI3t6euwqR8CJd3b61cXd9ghSYz/EaMPDJN1dxEl7Znjw5gy37Q5KOcfv7w44Zc8Qn9KCVE4RQ4v6TXJINeXaXjc6srczsHF+6PBBVR8QoSBVExSkyFboC5PeuNeNdki1ObIXhNIIQcFNhASfkb1U6DEpSGUpNZ87pJpzKk8jew2x9S/a7NOb64yT9j51Qz/V9tmhU4AbUx+2pYxFurdlk0Nq4p2dMNsmh9Q0Y2QPoEPKFA/emG1daK65tOtyyp4h5vHvYh1SjXFIOXaPBSn9u7BxjoJUP/AnQORTkKqB5tzFkFaipw+Z65DSkb323iwGUdSIKWlu8rP0GdlLHVLGS803Ttkzv87TDBs2ZW8e2bOwO7Axcq20l2UT+ibCD6W3m/BtuDENcGHkQKnmHJ952OT2W+qQapEgNfZC7Lrrb3QvJ67CrIMASLkcnHilFJoDwP5O3CEVtXi/1FYKR/YSV05jSs0da+XU0a4zngVQChg5Ni6MKEj1gulx/JaCVOWYvwsmrWZiWJCytCDVYvdCEEkqBpkkjezxxjudsucadB7FHVLrN35N7pBqioAz0TZ7NxZGLp8bZnJ7hJHg0zdm2E8iWNc5aS83WpBqK5um7C0rBd9xbbi2aocglSGyp8u0s/aukXJ58OaslEJzANjfdREJI8gm0BNbbxbskGpKZG/k9tshtePasCzFDqm+QEGqNihIka3Q/SymOqTSyF6Ln/gFoTTDIaUje2F7f5ZlETTAeTR0LUw3bPya2SGlY07N2LQulpoD8cjmBzO4PR68OUMkwKPuOA8g/40EieMpF4btLDQH5te1VcLwyeysIKWUwsUdtxWC1DRDqfnOwMbuwMY1RvaMcHDTKzGyFzutjib8XdaJiKQOqYkf5urpbFypuWPBC6JeRthPvBC7g/j3cG7ocE/QB1JBat/sOnqA+btg0mp02asxQaorkb0GCAp6DR5LzeFr55FBQWrk2DkcUs05lbu2glKrb+LrZt4hpQWpIR68sdntoeN6j74SC1J0FeTnetsdUmvcflEUD/TQNyj/P3tvHt3Kdd95fmvHThLg+ja+fZG1WIslWZJlyZISL1ndiU8SO5uPncyZpJNJJpnu6Uwy6fZJT3fSSZ+emSydpBN37NgZO0vHjh1bkhfZ2ldLftLb3yMfyUcCJAgSexVqmT8KtwCCBaAAAqhbQH3OyXH0Ht9jPRCouvd7v9/vr55YUEDWA4JUsaI6mpCbiIhI+5G9gVOuaMjLqhWb3CsTYVMczvg9UgMlL6soKhrmxgLmf3fwLMlXO6RocUi1c40OMyVFtdYREYnvuKC+kaXNIh79g6ewul3qxeX59ANfkBoY9OxifDxJo/tg0LDMEAhSmkFFBxCJ7Km+Q8pySLka2XNQak5+VjQ5pBiGgcjRM4nHmrJXvUeZm2sngpT5NcemfEGqW8zInncdUiJHpuztFlfJs8+u28UrDqmSojl6dsfDkuPeNZ/esV4Vzqd65JAarzqkejVp7+9fXcYri5me/F3DDHmWHJ+uum07EDIKsgqWcW9wUCO0OaAHSaEuok0ie3txir15I4vLqTwuJvO9ukSfXlPeMv/Xj+z1HTrucD6exe1ScyLkeLlDqqLpVDhcOJYBxzId2cmHFSL0uF1qruqGJY7ZoVYL6AUKBM16aBoNbQlSAhGkzA6pdgtJ4pA6Nk0EKfoFBtrIlSuIedghxTAMYgHedjKZVTZs41zwkiDVLrIHAJNh0S81dwHiSutdqbkpDm/1QJC6lMzh1z7/Ov7ymWt7/ruGHetZUj3c6KSPsKCoCIv0DIawXKOUOKAHSX1nYCTAQzdqBxPdQFy0Rb+Lil78DqmB4f4u2MfT1Dqk3HkrDYVDSjeoiOwBprDhC1K1SYOCi91eZOHXKkJJY4cUAEgCR82UvWJFg8Ax1s8yERah6kZbwSCZLYNlgKOTYQC+Q6obvF5qDgAzsQBSufKuXy+ScewedUgZhoFixalDSnQ0CMCnt5Boca9KzUmHVKaw9/fm7331AnQDfrGzA4ggdXy6c7dtQd49ydNNrF49Sg6cBkn9VFXyv3uZtEe63PxpfRRjOaRi7l7HCOALUj57oqy665AiG3Hdww4pVaej1BwwI2p+hxRQUatT9igQpEhPmx2aRl+HFECfQ6q+424qWp0a1maDncyWMRWVMB40N3F77YsYNQzDQF5WEfG4IDUdk5Cy6Rxr7CarxwuClKzqMAxncftERMJmob2r0Ke3kGjxZLQ3glQsKIBh9u6QemUxg8ffSgLwN9NOaIzsdSZIaQhL7qyv7aj16tFx4DRIStUpewAQJYLUHg6qyDPCF3UpprwN8EGA78092Kc5dO1ifDwHLVP2vKyhqJpOTeRK4Fm/QwpmFI5h3HUe1U4imy/8qHVIUSZI1YsGxG2QbtOJk8zKmIkFLEHFj+x1RlHRoOmGpzukAGAmGrBGttdTJJE9m1LzsaCAbLlC9fRX8ux2FNmLiFA03RdlBwwRzRPh3kT2ONacALm1B7HUMAz8x6+cx2REwv3HE1bptk9zktkyohKPmVi11Fx2/vrnZZWaQnPA2UHZsGLnkCrs4f1vCVKK/xmilvK2H9cbEL4g5bMnym6XmlffwTQv/Fuh6wZ0gx6Hix/ZM6lohquF5kDdNJtWDikKpgHaIfFcy+seJMWGSWhkYlW7qWHJbBnT0QA4lkFY5PzIXoeQ18vrkb2pmIT1nLzLHUQcUnbuhbGgAMOg21VX7ODZHa8KIpt+bG+grOdkRCW+pwd+E6G9xS+/eXEdL17bxC89chzT0UBH4sqoksyWMTMWsO6FnTxLiopqK3q7hTTCkb36IRBEJMzt4f2/XTLfB77LkGJ8QWpg0LEL9vEs5JTENYcU6+1Sc5UyQUHg/MgeYLrW3P6ZOJlmQ0rNaekgI5gTAuk49WuM7E1GSGSvnUOqjJmY+bWRAO87pDqEvF5ed0hNRwNQNH1XsTlxSAWF3ZvFWLU8OktxbK9Ert/BZjdR/cw4mU7p0zvSBaVncT3C2/bF8MK1za4O8XTdwO9+5QIOxUP4sXccQlji9uQQGRXWqs8SS8ToQJDKyxpVHVKjXGpekDWrM5CIi3t5/5PorB/Zo5jyNhAcd/sqRgJfkPLZE2TCRIB3563EebzUnDZBQeBYVPzIHhVF8066GlS/Q6otpYq6I7I3ETJ7VFp1SMmqhkyxgtlqxCIaEHyHVIdkh8QhRUTJZEOxOdmINHNIAaC6R6qkmJ9PJ5E9Ehlr17vm01s2cnLP4nqEx26awUZexmtLWx3/2S++cQPnVrP4X7/nJESeRUQS9tShMyqksjJmogEEBA4ix3Zcah6hqENqVEvNdd1AqaJZAn6t1Lz7e3zW6pCiS9x79XoGd3ziibaHdiOB75AaGHTtYnw8R6miQeRY10q5WY+XmhPxh5pSc46B6jukUNF0iC6JrATikGpZak46pChx2BEknqNmwVps6JDiObYaW2m+2CKdQTOWIMX7tvoOIQ6pmMcFqemo+R5o7JFqFXnzgiBlObwclZpXI3ttYq4+vWUjL1uOzl7x0Klp8CyDJ88lO/pziqrj9x+/iDNzMXz/rfsAABGJg6Lp1LhhaUTXDaRyZmQPMJ8lnbhtaZuyN6ql5uTwPdwQ2RvGUvOXrm1is6Dg6nrB7UtxH1+QGhh07IJ9PEtJ0ayuGzcgLhavFnET8cdtNw7BdEjRISS4iaoZrruOAoIDhxSJfFLy/iFIPEuNpb8xsgeYjo9Wp3+pqhtmuuqOiQYEy/Hj44xah5TXI3vme6Bx0l6xuokINyk1B+gWpEpddEi1GwTg01vSBcUSA3vFWFDAPUfjeOKtzgSpz728hOubRfxv7z1lHQRGelDsPOxsFhVUNAMzUfIs4TuM7NFZak7LgdOgaJyqaglSPSk1p2ttsZAuAjA77EadUjaNlXJv78E+9viClM+ekNXaGFQ38LpDirZSarNDypuvZS+p0NQh1dIhZf4ebVP2RJ6FQsmCtVTZ6ZACzB6pVsW+SRuHlN8h1RnDUmpORMlkdmdkj2xQLvbi/wAAIABJREFU7J5/nhCkGjZYrZB4DlGJbzsIwKd3qJqOTFHpuUMKAB47M4PLqTyubTh3QHzjfApHp8J46OSU9Wu1SWN0bahpgtw3ZqsOqUgHbltV0yGrOp2l5pQMLRkUjVNVAwILjmW6juzpumE9H2hzXy+mzfvCekNMfdQwdB1qRcH1orcP1byCL0j57In6qRNu4PUOqUr1ut2e6EYQOAYVSoQEN6noBgSXY5TWlL2Wpea0OqToieyVlN2ieSIittxcr21XNxFEkJI6O9X2GZ5S85DIIyrxu06Li4qKoMBZhyL1eEGQaiWo2ZGI7G06m09nbBYVGAZ6XmoOAI/eNAMAeLIDl9T5tRxu3jcGhqm937uZGjdqkKjvtPUsERwfbhRaTPJ0C+KQKo9YZI+4AImAzzDm9N1u3YF5RQXZttAm6C4Sh9SIO2JlzcAt8n/Da8d/0e1LGQno2AX7eJZSxV2HlOen7JHIHkUOKVK0Psqomu66yNNRqTklHWQE2qbsNYrmkxEJGy3s6MlcGSLHYjxkCgu+Q6pzcmUVLFPr3PAyUzHJinESCorWdKMYEjnwLEO1INVJZA8wY3t+h9Tg2MiZr/Vkj0vNAeDARAhn5mKOY3vZcgUrWyWcmo3u+HXLIUVZ5Igm1qoOqZ1uW2evFxEqaIrsBUbUIVWqVB1SdT+LvQw72a5ObWUYuiKv5YqGG9slAH5kjzjXvH6o5hXo2sX4eI5SRbcsvG7AVk/ruhlhTAPE4UJL5MqP7JlUNMN1kcdJqTm9Dik6puwZhoGibWRPRE5WUW7Sc5XKypiOSZYbIBoQUK7ofr9aB+TKFUQkfoejwqvMRANWjJPQyh3MMAzGggLdglTHDinJn7o0QMhr3Q+HFGBO23t5cdORyHhhLQcAODO3U5Cq9ej4glQzSGSPdNFFuhCk/FJz92l0SAGmc61bdxN5NsxEA1QJusuZIsj5/qhPVSWF9TR9/oYZTwhSDMP8KMMwX2AYZoVhmDzDMK8wDPPjNl/3cYZhLjEMU65+zSNuXO8oUa5oCLpYam45pDy6TyQOF7fjYQSBY/3IHgBV1yG47FpzUmpOa4cULZG9imZA0w2byJ65OWi2GUtmy9aJNlCLpvgjzp2TK6tDc7I4beeQklXbQnMC7YJUraTX2WJ7sk3M1ae3pAumIJXog0MKMHukdAP4+vlU2689XxWkTs3Gdvx6LyaNDTvJbBmTEdFa48UCnUf2aHJI8SwDlvFLzQHz59KtGEueDfvGA1RF9hY2zLhePCz6DikKHYrDDB274Pb8KoA8gF8B8AMAvgHgMwzD/EvyBQzD/BiAPwHwVwDeB+BNAP/EMMzNg7/c0aHscmSP7MO9GtmrUDdlj/EjezCFQrdFQqvU3EGHFEeZC0WqlpobLn8uLRdIw6abbPKaOT7WsmWrPwqoWbb9rhTnZMuq5wvNCTOxAFJZecf72a4sv55YUECWYkGqVNEg8qxjMTsRlrBZUDzrRvYaVmSvTw6pm/fHMDcWwBNvrbX92vOrWUQDPPaNBXb8eiTgl5q3I5mVdx1u5GXV0bORvK5OBg8MCoZhqDlwGiSk1Lz+ECISEJDboyC1fyKEimZQ4zhbqBaa3zk/4QtSviA1ULwiSH2/YRg/YRjG5wzD+LphGL8G4LMwhSrCvwXw3w3D+IRhGN8A8DMALgP414O/3NHB7VJzhjFPa7y6SFYpnLJX8SN75pQ9l0VCkVjjW07ZM8AysC1WdhMnheyDoFixX9ATh1SzkmYS2SOQBUnW75FyTF6uIDYsDqmoBFnVka0TJAuy2tJdNBESsFWk9/1SqpayO2UyIkLTDWSKvktqEGwUZIg8i2ifNkMMw+DRMzP41sWNptFlwoW1HE7PRnfFb8N+ZK8tjW7biMRDN2rup1bkKYzsAaZ7u917Ztiwd0jtPbK3b9x8b9DSI7WQLmAsKODEdAQbedmze6teQJyfkSE5WKMdTwhShmFs2PzyawCmAYBhmKMATgL4XN2f0QF8HqZbyqdPlCoaAry7pzccy1jCjtcgkSuemil7prNl1KlouusOKY5lIHBM61Jz3f2uKzucuLsGQbOenKmqIGXnkMrLKvKyumMTEfOnSXVMbogcUlNVl0oqW4vtFZXWDqmJEN0l4O2uvxHi1Bn1XpFBsZFTMBkW+9rB9uhNMyhVNDx7xW6JbWIYRlWQiu36PeIW8QWp5piCVO1wg7htncQcaSw1B6qR/BErNScOqfpS84jEdx1XJYcV+8eDAOhxGS6mizicCGEqKkHVDWxR7PLtN6Tbi7bP37BC307GOfcBeKv6/5+u/u/5hq85ByDOMMzUwK5qhNB1A6msbC3W3YJjGeiejezR5ZASecYvbgYRetz/mUg817rUnAInlx20FJ9ao+13OaTMyJ5dJ07Kmopks4mgZNHoBYZJkCLiZKouwtBWkAqL2KLYTVSqdOZubiXi+vSejbzct7ge4d6jcUQkvuW0vZWtEnKyitMNheaAufYKiZzfIdWEiqZjI6/Y9hE66ZGisdQcoGuK7qAo2hxuhSV+Tw4pgWMwWb2v0rK2WEgXMJ8IW/u6UY7tkQNIX5AaDJ4UpKpl5T8I4A+rvzRR/d+thi/NNPy+Tw9J5WQomo4D8ZCr18ExDDSPOqRoKzXnWdazbrNeUtEMKlxrgTYLP1U3qCs0B+oEKZdPUUmsoNEhFRI5BAQWGzaLrcYx3UBnmwgfk1y5Mjyl5sQhlat3SKk7TssbiYdFFBSN2mhLSems/7HmkBrdDcogSRfkvhWaEySew7tPTeHJc6mm0Zzzq2ah+enZ3YIUUN2UUzQljCaIgL0jshcg8W8HDikKS80BeqboDpKioiEg7Ozci0o88oqzPrBGtksVjAUFS2ykwSGlqDpWMiXTIeUfQNQcikNysEY77u+4OoRhmMMAPgPgHw3D+GTDbzfeFZgmv07+rp9jGOZlAHeurq728CpHg+WMOY3h4ETQ1etgWe8KUvSVmvtT9gDTeeT2lD2g/bQ6TTeoee/UY/Vfud0hZdP7AJj9KYmw1MQhtXsTEW0T2VvYKOCvX1jsyTUPA4ZhDJVDarr6Xkhma4vzgqwh3MJhNB4yxThae6Q6juxF/BPzQbKRU6zXvJ88dmYG6zkZry83nueaXEiagtTJGXtBKirxfpS5Ccnq4casTfzbiSOmIKtgmdrEXVoY1VLzxs7AsMTDMGrrjE7YLimIBQVEJPMe7KRTrN8sZ4rQDWA+EbYOIEb5fp+vfv7cHNw1StB1l2sDwzBxAP8M4DqAj9T9FnFCjTf8EfLftk9awzD+1DCMuwC8Mjc318tLHQmWqoLUgQmXHVIejuyRDXG8zyehThF4Boof2aOmm6ndSaTpkHL/OhupdUjRGdkDTMeH3elf0sYhFWnjkPrsS9fxG/9w1hLpR51yRYeqG0PjkIpIPMIiZ4mVum5UI28tHFIh855Oa49Uu+tvJBbgIXIs1kf4xHxQGIaBdKH/kT0AePjUNDiWaRrbO7eaxYGJYNPP8l5iS8MOiX9P28S/nbht87KKsMj3tUesG0ay1FzeLeBHOhAXG9kuVTBOmUNqMW2uXw5PhvzIHqqfP4m+z9+wQt9OpgkMw4QA/BMAEcAHDMMo1P026Y463fDHTgPYNAxjfQCXOHIsbZYAAAdcdkh5ObJHIiDT0UCbrxwMAsv6HVIgpebuP4REnoXcYuGnaXQ6pMiUPbcL8ptF9gBgMizaFjQnszLCIrcjJiHxHESebeoEIELF05ealwOPEmSzNSwOKcB0SZH7dan6vmrlkJqoHjLQ2iNlRvacLwEZhsFkRMRGjs5/zzCRLamoaEbfI3sAMBYScM+ROJ48Zy9INSs0J0QknpoJYbSxtm1zuCE5H5BRqG6IaWM0HVI2gtQepkxakT2KBgMspM1t9XwijKjEQ+JH+wAiX1api8sOM54QpBiG4WFOzDsB4H2GYaTqf98wjKsALgL40bo/w1b/+58HeKkjxXKmiOmohIDLdkbWww6pVFZGVOI7KpftJwLHQjfgWYGvV6iaAYEC55EkcCi3WPhVdJ2K8vVGJOoie7sXFYmIiLSdQypXxszYboE4FuCRa7JoJELF05d9QQqo9aMMkyA1FZUs4dF6X7XpkAKATVoFqYpm+7loRTNXoU9vIZvAQQ2MefTMDC4m81hMF3b8ermi4epGoWl/FGA6pJrdF0edZE6GwDGWWxKo3RMdTdlTNIQlOtaG9ZjO7dESIQs2kT1LkOoiskoEqQhlDqmoxCNRne45FZVG2iFVUHxBapC4v+Nyxh8BeD+AT8Ccmndv3f+RJ/ZvA/hZhmH+D4ZhHgbwFzAFrP/gyhWPAEubJdfdUYC3HVLrORlTMXenFNYj8Ka4MeouqYpGh9ATaOeQorRDqhbZc/d9RJwstpG9iNkh1Vjmm9wuY8bGsRgNCG0dUs9eSTctBx4liEMqNiSRPcB0ORDh0RoB3uIwhnRIZSiN7BWVzqbsAeZnxhek+g8RyhPhwawNHrtpBgB2xfYup/LQdMN2wh4hGvAje81IZsuYjgbA1j2jzQie8yl7VDqkBNb1gSWDptRjh9RWsYLxkIgQ6ZCi4DO0kC5gfjJkRdRGXZDKlVW/0HyAeEWQ+p7q//4XAM81/N8cABiG8VkA/xOAnwHwFQC3Avg+wzDODvpiR4WlTBEHXZ6wB5gdUl7VT1K5sjXBiQbEam+SL0jpVEw+lITW1nj6p+y5e4paqgoHdpG9M3MxaLqBL5/dOdAimStjxkYkjgb4ppuIVE7GREjAZkHBW6vZHly5t8kNoUNqOiohlZNhGIYVUWrlXpiouiIylJaalxS147LWyYjoC1IDgESJJ6OD6ZY8GA/h9Gx0lyB1Ya31hD3A/AzQEDeikWR297OEZRlEJN7ZlL1qhxRtjGJkr6DsdpSGuxSkNN0c+hELCpB4DgLHIE9B7HUxXcR8Imz999SIH0DkZd8hNUjc33E5wDCMw4ZhME3+b6Hu6/7MMIzjhmFIhmHcYRjG11y87KFG1XSsbpdx0OVCcwBgWUDTvflwTOVkavqjgNq0v4o22i4PlRLnUbtSc7NDir7bOJkK5Paitaho4FnGmvpXz/tvmcPp2Sh+76sXrK4rwzCQzMo7Oj8IkSbTpMoVDdulCr7/tn0AgG/7PVLW6zRMp4szMQlFRUNeVlGqVIXOFptFgWMRDfBUlpobhlGN7HXukErnd7sKfXpLumBuAgcxZY/w2E0zeGlhc4ej7/xaFiLP4nDdJrWRiCT4glQTmj1LohLv6DXLyxqVDqlRLDUvKequ+2Un8ct6yMHWWNB00YYl3nLduoWq6VjaLOJworanmxxxh1TBF6QGCn07GR9PsLpdhqYbVET2eJaFF/UTwzCQyspUOaSE6sZdHXGHlKrRNGWv+cKPXocUPZG9Zi4QjmXwr957GovpIv7mpesAzF4HRdXtNxFNHFJkwfa2fTGcmoni6cv+DI1aqfnwRPbIwUEqJ9ccUm0EnYmQiAyFHVKyqkM30HH/42REgqob2C7R6foaFjZyMlim5rIbBI/dNAPdAL5xoVbRen4th5MzkZbPwojEQVF11wdY0Ehyu9zkWSI4juxFqOyQGk2HVKMj1pqQ16GYtFV1zY4TQUp0JlD2k5WtElTd2OWQ2iwqI5uYyJfpjMwOK+7vuHw8yVJ1vDkNkT2WgSdPbM2Tdm3HSGC3ITE1ZUQfQISKTseUPYnnWnY1qJRcZyO1UnO3I3ute3IeOjWFe47E8V+evIS8rGItu3sqEiEaEGxPQusnZT5wYhIvLWRG7vS4kWGN7AFmX1irsvx6JsIilZG9knX9HQpS1ddglGMcg2A9ryAeFgd62HDzvjHMxKQdsb3zazmcmmk+YQ8AVWPraaIgq8jJaovDjfavV1Ghc0M8iqXm5lRS+1JzJz/LeoigTxxS5qRKdz8/C2lzT1fvhpyKSjAMUOnyHQR+ZG+w+IKUT1csb5YAgIrIntkh5T1BKlV1VtAU2at1SHnv9ewVmm7AMEBFh1RAaL3w02h3SLlcfGo3qrkehmHwv7//DNIFBX/6ratIVsvJm3dI2QhS2dpErAdOTEJRdby0sNmjf4E3yZUrYBggQmH/SbdMx4hDqlwrNW8j6MRDApWl5qTsv1NBaqoaIRvlUeCDIJ2XB1ZoTmBZBo+emcFTF9dRrmhI52Ws52ScaVFoDuyt2HmYIes7u2dJxKEglae11LxaJWB4dLp1pxiGgYKi7nJISTwLnmU6FpMsQSpEInuc5bp1CzJhsz6yR6Z8jmJszzAMX5AaMO7vuHw8yXKmCJYB5sbdF1NYhoHmwQdj/UaWFshkuVG16AK1fzsNU/baOqQo7ZASeTo6pEoVrW0s6e0Hx/GBW+bw59++irMr2wBaOKQUdZcb0xKWYxLuORKHwDF4esR7pLJlFRGR3zFdyusQJ2sqK6NAHEZt4jQTIZHK02Xi8Oo0sjdVLdkexQ3KINnIywMrNK/nsZtmUFQ0PHc1bRWan2pRaA74glQz1rZN5+xss2dJm9dL1XSUKzqdpeYCB8MYnYNLU3zbPa2XYRhEAp3H7RodUmGHnWL9ZGGjiKDA7diPjLIgVa6YsfZh6sGkHfp2Mj6eYClTwtxYkAoXCccynozskVNmqjqk/Cl71r9doEDokYQ2peaUOqREiiJ7Tlwgv/a9p6CoOv7oG5cBwDZGG5V4GAaQb+iLSOXKYBlzRHtI5HHHoYmRLzbPldWhiusB5s8/ILBI5crW9EYnkb0tCjukSg4jh42Qkm0yBc6nP2zklYEWmhPeeSyBsMjhibeSOG9N2Gsd2SMbNrc31LRhRbk76COsh4jerSZ5ugWJ5JdHJLZHHFB24mCkCzFpq7S7Q8rtyN5iuoD5RAgMU1tPjrIjNiebPyPfITU43N9x+XiSpc0iFYXmQDWy50mHVK17hhb8yJ7pOgJocUixUDS9qeCq6joV19kIxzIQOMb1oltzklj7BcWRyTB+/O5DKCgaJkKCFTmshwgsjVGLVFbGZESyhMF3nZjEW6vZke7ZyZUrQ1VoDpin4TOxAJLZWql5s8J8QjwsoqBo1HWKdRvZGwsKEDhmpN/bg8CNyB5gOnLffWoKT76VxLnVLBJhsa2DO+w7pGxJWn2E9ocb2TaRPRILpnFDLAl0RPIHBXGU2vVRRiS+4yl72aogFdsxZc/dZ8RCurBrmuYoO6TIz5TGz9+w4gtSPl2xnCnhAAX9UUA1sudFh1ROhsiziAXpueH5Dimz0BwAJVP2Wk+r03QDPIUOKYCOSTxFpX1kj/BLj5xAWOSaCsREYGlcfKZyO0d7P3BiCgDw7JV0N5c8FAyjQwow3aypXNma3tjOnUimpG1RVmxONrudRvYYhkEiLGFjBDcog6KkaCgomiuRPcCM7aVyMr5ydg2n2/RHAaa4Avil5o2sbcsIi5ytMB8N8FBUvaWD2HLlULghpmVoyaAgYlGvHFLbpQoknrXuvxGJc1XQ1XQDS5slHJ7cKUgFBA5RiR9JQYocOvmC1OBwf8fl4zlkVUMyV8bBOEUOKQ8KUqmcjOmotMMi6zZ+h1TNISVQIPQEhNYLv4pmgKMgWmgHDZN4Sorq2AUyFZXw+x96O3750RO2v19zSO0UF8jnmHDL/jGMBQU8fWm9y6v2Pjm5MqSCVACpnIyC7Ox9NVEtraWtR6rbKXsAMBkVfYdUHyGv7aQLDikAePjUNDiWQU5W207YA+ocUh26RIadZK5s20UIND/cqCcv0x/Zc/vAaVBYQyxsfhbhLibkbRcrGA/VhEryd7hVEr+6XYKi6TsKzQlTUWmkI3s0CsLDCp07GR+qWcmUYBh0TNgDvCxIlanqjwLqHVLeez17RS2y5/7t0dsOKdZ1Sz9xsjjlvTfP4v23zNn+XrPI3nquvKNzimMZ3HcsgacvbYzMFKJGTIfUcEX2ALNbLJWVzemNDjaKE2HikKJMkOoysgeYPVJ+h1T/sAQplxxS4yER7zg8AQCOHFJ+h5Q9a9vNBSniumg1aa9Vb5Hb0DJFd1AQh1TIZi0RCfDIddwhpViF5oApeqi64ZrAt5guAgDmGyJ7ADAZlUbaITWMB2u04v6Oy8dzLGdKAEBPhxTDQPfgxi+VlanqjwLqOqRG5OTLDhLZEyjoZrJOIpss/FRdB0fBddohCXRE9ux6H7qBLEyydQ4pVdORLiiYavgcP3BiEje2y7i6UejJ9/YawxvZCyAvq9jIy442ivGqILVJmSBldaJ0GNkDiCA1ehuUQUHEPjdKzQmP3TQLALhpzoFDSvQFKTtWMiXsb7JGjjoQ8fIUR/aIc3tUSs2tyJ7NzyLSRSH5dqmyQ5CKuBx7XUib65TDk/YOqVGMaOd9h9TA8QUpn45Zyphq+sG475DaC6mcbDvNy00E3o/sWZE9GhxSbSJ71DukXF6wlivOpuw5gTh+6k+1N/IKDGP3pMx3HTd7pJ4ewWl7hmEMZak5UCsoXkgXHAmdJJaRoSyyR0rWuxFrJyMS0nllZN1//SZdFfsSLgpSH77nEP74w3fgbfvaC1IcyyAkcn6HVB2KqiOZKzc9tCX3xmyLSXtUl5qPnEPK/FnYlpoHOi813y6puxxSQM2VM2gW00VIPIsZmwPyqchoOqTyfofUwHF/x+XjOZY2SxA4pqkdedCwLAOvJczKFQ3bpQp1kT2+2kdU8aDA1yuIGEeD0EMWfuWmDimjbbGyW5iClHsL1oqmo6IZXblA7LA71bZGezd8jg8lQjgYD+KbF1I9+d5eQlbN131YHVKA6X5w4pAipeYZ6krN9+KQEqFoOrIlX4DoB8R9lgi7E9kDzDLj990y57jfMtxFsfMws7pt1lrsH2/tkGoV2SMbYifR4EHT7qBs2CBCkd09PyzxKCha00nIdmwXFYwFa5/vcFXocusztLBRwHwiBNZmLTkVlZCTVeomxfYbIjIO4zqGVnxByqdjljJF7BsPUrMR5hh09DCgAXLi0G6k8qDxI3s1QYoGh1S7UnNVMyBQW2rOuXqC2mpUczeQqWr1peaprPk5nrYR57/v1n146uI6VrdLPfn+XoFssmJDuJAjjlbdcPa+EjgW0QBPXal5UdEgcmxXPXnWKHA/ttcXklkZY0Gh4wmIbmJOGhutDWsrVqq1Fm0jew46pGh0aIxqqbndPd+aMqk4F5MaI3vEIVXs4O/oJQvpgm1/FGA6pACMnEsqL1fAsYz1XvfpP/4r7dMxy5kSNYXmgLnxLXlMvU9Vb+60dUj5kT3TdQTUJg66SbtSc1U3qO2QEl2O7JWt4ubeLOgZhkE0wO841a59jncLyz9x9yEYAD77wvWefH+vQAS7oYzs1d2vww6FzomQiAxlHVLlSvfdamSD4vdI9YeVrVJTZw2tRCQe+Rbxs1GD9Kw2WyfX4t/NX7OCrIJlunMx9hsilo6OINV8CESnpf4VTUdB0WwFKTccUrpuYDFdtJ2wB4zuAURB1hCReKqmoA87viDl0zHLm0UcjNOzYJqfDOF6ugjVQyLKejXqQ5tDqjZlzzuvZa+pRfbcvz3WTiKbdUjpVEQL7ZB4FoqL76OaQ6p3P8eI1ChImZ9juwLig/EQHj41jc++tDRSn6fcEFvdY0EeYvUzGXLoXJgIixRG9tSuN7qT0dE8MR8UK5kS9nlMkApLnGv9NzSyvFUCywCzY3uZsqchLNK5ISbrklGJcRFHqZ1rPtxhIXm2ZD4LSL8gUF9qPvjXczVbhqzqODzZxCE1ovf7XFml0p04zLi/4/LxFAVZRbqg4ABFDqkT01Eomo7rm0W3L8Ux5OZOXak56ZDyWilXD6mVmru/ELS6GrzYISWwLkf2qjZ7oXeLimhA2BnZy8mIh0VLpGjkJ++dx3pOxuNvJnt2DbRTE6SGzyHFMIzlhrMbAW5HPCRQV2peVLov+5/0HVJ9wzAMrGyVqJlg7JSIJPgdUnUsZ4qYjQWaxv5FnoXEs8i1eM0KskrthK92zu1ho6ioTbu8og7ExXq2qoLUToeU+Xe7MRjgYjIHwNxH2TGqglRB9gWpQeMLUj4dsbJlWpFpWjCdmI4AAC6l8i5fiXNSORksAyTClAlSfmQPqk5Ph5RVau7JKXucqwvWvUwSa8auyF5WbjmY4MGTUzgYD+JTzy/07BpopxbZG87FHBnm0YlDirYOqb1E9saDAjiW8QWpPpAtqcjLqgcje5wvSNWxkik17Y8imIcbLUrNW4ggblM7KBsdh1SzA4hOJ+Rt2whSERcje5eqgtTJmYjt78fDIhhm9ASpvKxaQqHPYHB/x+XjKZaqLqSDcXocUseqgtRlLwlSWRmTEYk6d4sf2au5w2jokAo4cEh1U0w8CCSXO6Ra9T50S6xBkFrPlVvGbjmWwU/cPY/nr27icirXs+ugmWGO7AG1vjCn76uJkIgtyjqkiorWdWSPZRkkwiI2cnT9m4aB5S1zfdVOzKCNSIB3xd1BK8uZUtsUgfksad0hRatDYxRLzZsdQNTEJGexbCJIxeoEKdJz6Y5DKo+pqITxkP1UT4FjMRESR+4AIi+riAyhy5tm6NzJ+FCLJUhRFNmLSDz2jQW8JUjlytTF9QBYbhtlhCN7NE3Za1tqrtHdIeXmgrW0h9H2zYgGBOTknZG9doMJPnTXAYgci08/Pxrl5tkhLjUHaoKU01LzeFhEQdGo6lspKt07pAAztjdqG5RBcGPL7KTzmkMqLPEt42ejhKrpWMuW2/4MIw2HG40UZBXhHg3k6DUix4JhRswh1eR+WROkHDqkirs7pESehcixKCiDfz0vJXNN3VGEqYg0kg6pKKWC8LDi/o7Lx1MsZ0oICCwmI/Zqulscn4nikoccCE42sm7AMAwEjvFUQXyvIR1SNAg9rUrNdd2AboA6lx1BEjhXO6RKfYjs1Zea67qB9ZzcVlhORCR84NY5/N0ryyPhIiCvD62n+3tlmkT2HG4WJ6pqezEjAAAgAElEQVQnz1sUFZuXK913SAFmsbkvSPWelYw3HVJRiYei6lBGxDHTirVsGZputK21iAb4lhGtgqxR2yHFMIzrB06DpCi3EKTIlD2HUybtInsAGQww2PWBrhu4lMrj5Ix9fxRhKiqN4JQ9P7I3aHxByqcjljJFHJgIUTf548R0BJdTeei6N5w9piBFn0MKMJ1Box3Zo8kh1Tyypxn0CGd2kMieYbjzmexHZI90SBmGgUxRgaobjj7HH7n3EHKyii+8fqNn10IrZDoNrULpXuk8smduPDIUxfb2EtkDgMmIiI08Pf+eYWFlqwSJZ5EI03Xg145OJ40NMysZs2e1bYeUJLSO7CkqIhRviPvdEblVVPDu3/sGXr2e6dv3cEqxojY9gLAKyR26m5oLUoOPva5slVBUNGeC1Kg5pMoqItJwurxpxf0dl4+nWNos4SCFp3cnpiMoV3SrdJ1mNN1AOi+37J5xE1OQ8oaw1w9UnZ4OKZ5jwbGMbam5Vr1OjqXzNi7xLHSj9noOGhLZC/V4yp6mGyhXdKTIpEwHTsc7Dk3gzFwMn3pu0TWBblDkypWhdUcB3ZWaA6Bq0p4Z2ev+ZzQVMU/Mh/29PGhWtkrYPx6k7sCvHW6WMtPGMhGkehDZc3qPcYN+d0ReTOaxmC7iq2fX+vY9nNLKISXxHESOdTxlb7tUQVjkdh14RqTWjrl+cLFNoTmBCFKjcr83DAN5ygXhYYTOnYwPtRCHFG2cmCGT9uiP7aXzMnQDFDukGCgj7JAicUWeEqEnwLO2DilLOKPUieL2aGgS2QuIvfs5kqLuXLlSE6QcdMExDIOfvHceb61m8er1rZ5dD43kyurQFpoDwL1HE/j17z2Fe47EHX19vCpIbVLkkNpzZC8iQVF1vzeoxziZzkYjviBVgxyK7msjSEUDPPKtpuxRXGoOmJP2+hnJT2bNPrWXFjb79j2c0qpDCjDFRael5lvFyi53FFB1SCmDFqTM3t3j060dUpMREfII3e+LigbDqMUxfQYDHTsuH0+wXaogV1ZxME7fgun4lHlDvZSkv9icbGSnKOyQAkyH1Ch3SBF3mEhBZA+odjHZiDqWcEaBk8sOkXd3NHRRUcGxTE9/jkRoyZZVpKoLZqfC8g++fR94lsHXzyd7dj00kpMrQy1IiTyLX3j4OAIOI2+kvJYWh5RhGCgq6t4ie1FTZNsYsRhHv1nZal+GTSN+ZK/GSqaEqajU9v4QDQjIK6ptzYSq6ShXdGpLzQEgwHO2zu1eQQSp765suz4QoqA0j+wBpP/JeWQv1kSQclqM3isuJXOYjQVsBbJ6SJpjVO73RFintcNtWKFjx+XjCWicsEcYCwmYjkq45IFJe6lcdSNL4ZQ9wI/sqTpdQk8zazz9Dinz8eKW266k6AgKXE/jL7YOKYfCcljisX8iiOub9MeK94LpkPK7Fwik1DxDSam5ounQjb2V/U9FzPf8qPWK9JNyRcNGXvakIGUVO/uCFJa3im0LzQGzCN4wgLyNK6ZYFWBoLlXut0NqbdtcJ1c0A28sb/ft+7TDMAyU2jmkJMFxZC9bquyYsEcIixyKg47spXJWuqQVo3a/J/cxmh2Kw4gvSPk4Zrk6AYbGyB4AHJ+OeEOQypKNLK2C1GhH9ogYR5cgZVNqTnuHlNC8kH0QlCpqTyfsAbCEllxZxXpORlTiO/oeh+IhXE8XenpNtDHskb1OETgW0QCPTUocUqUelP1bDim/2Lxn3NhyVoZNI35kr8ZKpuRIVIxa09l2v2YFDzg0+l1qnszJVtzZzdieoulQdaPlzyLSwYS8rZLSPLK3h89PSdHwyWeuWevCdui6gcsOJuwBNYfUqEzayw/5pGBaoXMn40MlpKyRxsgeYBabX0nlqS/eW7cie7QKUqMe2atO2aNE6JF4ztayTr9DyuUOqTanmt0QrXMCpHJlTHXocjwUD+F61Wk6rOTKFd8h1UA8LFIzZY9Mn9zblL1qhGNENiiDwGn3EI1E/MgeAHOTf2Or7OjQtv5woxFvCFL9LTVPbpdxfDqCE9MRvOyiIOVEwO+kkHy7ZN8htddS8y9/dxW//cW3HL9WS5kiyhW9baE5UCdIjYhDquA7pFyBjh2XjydY2iwiKvFt88ZucXwmirysYq2aPaeVVE7GeEiwNuy0MfKRPc0AywAsJUJPQGjikNKIQ4qO62yERPb6uWhtxV5H29tR20RUkMzKmOmwB+5QPIRMsYJsi3HfXidbVhHzHVI7GA+J1ET2SNn/XtyDEyERLOMLUr1kxeF0NhohwonT2NKwsp6XoWi6I5dbffy7EdIlRPOUr2bO7V6xli1jNhbAXYfjeHkxY9u1NQgKDgSpTtxNzQSpsMShoGhdH6ifX8sCqBkH2kEKzU84cEiNBwXwLDMyghQpb/dLzQeLL0j5OGYhXcTBeIjakcQnpquT9igvNk/lytTG9QAzslcZZYeUroOnpNAcqFrjbafs0dV11YjrDqmK1ofIXm3jlcqVO+6BOxQ3T86vp4fTJSWrGhRV9yN7DcRDAjWl5qUeOKQ4lkE8LPmCVA9Z2SqBZYDZMTqHnbSi5pByt3zabWq1Fu0FqUiguYhHuoSoLjUX7J3bvcAwDCSzZczEJLzj8ARyZRUXXZqgXap2fLUqNY8GeEcT6MoVDeWKjvFqr2A9YYmHphtdr5fOr5mvj3NByvx6sm9qBcsySETEkRGk/MieO9Cz6/KhnoV0AUcmw25fRlMsQYryHqlUTnZchOwGAsdCcUlEoAFVMyBQ5DqShHal5nTexl3vkOpDZI9sEMwpe3LHwvKhRFWQGtLYHtlc+ZG9nUyERWo6pIrWif/eFtuTERHrOTr+TcPAylYJs7EABIoOQ5zCsQyCAoe8TIcL0C2IGHDAgcuNuEjthAwvTPnqp0Nqu1SBrOqYiQVw13wcAPDSQqYv36sdRGRt6ZASedsusEayJfPzYTdlb689bBcsQcrZ2uJSMod9YwHHz+qpqDQyHVIFxRek3MB7Tz4fV6hoOpYzJRyepLPQHAASEQnxsIjLLp2kOKWbjewgETjWEjtGEVXTIfD03BqbLfxUP7LXkn5E9jiWQUTicWOrBFnVOxaWLYfUkApSi1XnF639eG4xERKxRUmHVC8ie4D5M/YdUr1jJVPyZKE5IRIY/Nh62iCClLPIXi3+3QjZENMtSNk7t3sBqd2YHQvgYDyI6ajkWo+UEwE/GhBQqmhtkwXbVUHKLrJH/v5uetg2C4o19beTyJ6TuB5hKjI693tysEbz528YoWfX5UM1S5tFaLqBI5Pt7Z1ucnwqYhvZu7FVwof+5DksubwRNAwD6zmZ6g2bH9kzqHIdNSs11/xS85aUKxqCfYg8RAM8rqyb95hOI3vRgIB4WLSEm2Hj6+eT4FgG9x1LuH0pVBEPiygoWt8iLp1AIih7FWsnR2iDMghWtkqeLDQn7LWUeRhY2SohHhYduQ8jLXq3iLAXprlDqolzuxckq5OoZ2IBMAyDdxyO42WXHFJFK7LX/GfhtPSbCFLjtg4p8+/vJvZK+qMmI6I1HKEVmm7gynreUaE5YSoqWT+XYacgqxA4xjpU9RkM/qvt44iF6qjyIxQ7pADg+EwEl2wm7f3Zt6/ixYVNV8fHAuYDSdF0ygUp70f2/tNXL+C5K+mu/mxF1SFQ1MskNSk1Jx1SHEXXWg8dDqneP+KiAR5X1837YTef44PxkOvCeL/42rkU7pyfsO3IGGUmqq/HFgXF5kUHJb1OmIyI2MjL1E+19QKabmBtu+zJQnNCZI9j64eB5UzJ8c8wJHLgWMY26uWFKV8BgevbYVNyu+qQipkO5LsOT2Blq4QbDsSWXkPul63Ewdkxcx3QbqBSK4cUceMQd1wnnF81UyEPn5rGja2SdVjZjOubRciq3pFD6uBECOs52eogHGbysoqwxFPblzys+IKUjyOubZgbqMMJejukALNHartU2ZF13i5V8LmXlgAAq9vuTuAjttrpGMUdUjzraYfUVlHB//uNy/jC6ze6+vOqblBVFC7x9gs/sugQKHJz1SPy7nZIFRV1zz05dkQDgrWw7KYLbj4eGsrI3nKmiPNrOTx6ZtrtS6GOiZC5AckMOLb36ecX8cE/egZq3f2cRPb2LkhJKFd0awqVT/cks2WouuHpyF5Y4hz16AwzK5mio0JzAGAYM/5tF9kryipYZu8uxn4i8SzKle6nwrWCCDvEgfyOw2aP1MuLg3dJEYdUK7c1WQck2+wvyIFEK0GqG5fhhbUcEmERbz80DlU3C+FbQQrNT3YgSA17/2U9eVmlWgweVujcyfhQx8JGAdEAj3iY7pPvE9PmDfZyXbH537x4HQVFg8AxWN0e/AlLPamq5ZXqDimW8XSH1OvL2wCAzUJ39uKKplMl8kg8C9km6lPxSIeU4pK4Wa7oPZ+yB+w8te40sgeYPVIrWyVPi752fP18CgDwyJkZl6+EPiaqz81BT9p74domXr2+hS++URPnyQl3oAeCFABsjMjkpX5CYjZed0iNcmTPMAysbDl3SAGoClL2kb2wSLdDQ+JZ6Ab6slZMZsuYCAlW7P/0bBRhkXOlR8pySLW4X5LJmHtxSNUmVXbhkFrL4tRsFAcnTNGoXY/UpQ4m7BGIGWGxmpYZZvJlX5ByA3p2XT5UQybs0fyABIAT1Uw0EaQqmo5PPruAdx5N4Ph0FKtbbjukqic/NAtSHIuKhyN7ry9tAUDXU61UjTKHVJPIntUhRdG11iNVT3fdcEipmg5F0/tywhytTkcKCCyiXSxaDiVC0HTD9XtRr3nyXApHJsM4NkV3z6AbkIOczQE7pMiJ/R9/8wr06v3Ciuzt8bNh9ab4PVJ7hkSRnLpraCQi8V3FjYaFdEFBuaJ39DOMBnjbKXsFWUWI4v4ooL8dkclsGTN1KQKeY3HH/IQrk/bI/bLV4VY8JELgmLYdS9stpuyFuxSkNN3AxWQep2aj1nuv3aS9C8k8DkwEOyrtnq86pIa1/7Ie3yHlDr4g5eOIaxsF6uN6gCn0RAO8VWz+5e+uYnW7jI8/eAT7xgKuR/bWPRLZUzQPO6SqglS6W0FK16kavU0ie43WeKtDinKHlBsdUsUexZLsINORpqOBrgR6MmlvcXN4Thrzsornr6TxntN+XM+OcSuyN9gOqbVsGWNBAReTeTx5LgnAjOyJHAt+j/c43yHVO4ijwcul5mGJH+nIXm3CnvOe1VhAsI3s5RWV+glfkkAi+b1/vq9ly5briHDn/ATOr2WRtXm9+klRUcGzDMQW90uWZTAdDbSNym2XKogGeNs1W0Qkkb3OXs/rm0WUKhrOzMas+4cTh1QncT0AGA+JGAsKQ7VuaUZBVhEJ0P35G0bo2XX5UIusaljZKuHwJP2CFMMwODEdwaVUDoZh4M++fRVHp8J46OQ0ZscC7kf2cjJCIke1+i5y3u2QMgyjLrLXnSBV0Yw9b9Z6SYAs/BpOImmfssezDFjGnSl7ZQenmt0Sqy5UunU5zg9hF8PTlzagaDoe8fujbCGl5oOM7BmG2SXyL+44gIPxIP7wm1dgGAZKimbdU/bCZNT8N/mT9vbOylYJEyGhL513gyISGO3I3kqm89hlJGAf2St6wKER6KtDSsZMQz/jOw7HYRjAqwPukSrIGoIi1/bwaXYsgLU2B97bpYptXA+A5YgrdvgZulCdsHdqNoqAwGEqKrV0SKmajqvrBStN0gnzidBIOKRyMv2C8DBCz67Lh1qup4swDPon7BGOT0dwOZXH81c3cXYli489cBQsy2DfeBCZYsXVKRGpnEx1XA8whQTVo4LUje0yNvIyJiMitoqVrv4dqq5DoEjkaWaNJ90NtDqkGIZpWsjebyybfR8je930RwHATDQAkWNxfYgWdl87l0Q0wFvlsz47ETgW0QDftUjeDdulCmRVx/6JIH7+wWN4fWkLz11Jo6RoPRE+4iERDAOs5wcbQxxGVjIlTxeaA6bDQ1Z1zx5m7ZWVLfN+3snPMdpExCtUO6Rohjikyj12SFU0HRt5GTMNDqm3HxwHxzJ4ecCxvZLi7GcxG3PmkCJu2UYEjoXIs8h3GHs9v5YDw9QKyg9MBFs6pBbSRSiajpPTnTmkAGA+EbYmrg8zBVntqo7BZ2/4gpRPW65tmDcgL0T2ALPYfCOv4Pcfv4B4WMQH79gPAJhzWDzYT1LZcleTuQaJOWXPm5E9Etd790nTqdFNRKaiUtYh1ST6Zk3Zo8jN1Ygk2Bey95teTRKzg5xcd/s5ZlkGB+LBoXFI6bqBb1xI4aFT01S/F90mHhYHOmWPPOdmYwH8yJ0HMBWV8EffvIJiRevJ54LnWMRDohVD9+meTsuwaYREXLopZR4GljMlRAN8UweMHdEmDilz7DztHVL2zu29sp6TYRjmfauesMTjbftieGnAxeYFRXV0v5yJBbCWLbecOtjKIQVUe9g6/PycX83hcCJsucEPTISsIQl2XOpiwh7hcCKElUwJioc7Zp2QL/sOKTfwV48+bSGK+BEPRPYA4HjVivryYgYfuXcegapLgmTSV1vcrPvNek62ymBpReBYKNruziIv8PryFkSOxQMnEgC6i+1VqOuQIl0NOxcB5CSaVocUUJ0Q6KJDKtAXh5S5oNzL53g+PjzW9+8sb2Ejr+BRP67XkvGQONAOKRIfmR2TEBA4fOyBI3j68gZeXcz07HNxei6KF66lPfmsoAXDMEyH1Lg3HOjNIBs4O4FlFFjJlHCgg/4oAIhIZodU4+en4IUOqT5F9ojLaHZs9/P1rvk4vrO0NVBBpKhojgrmZ8ckFBXNtqSesFVUWgpSYYlDocMOqQvJHE7P1sSlAxNB3NgqWQeWjVxM5sEwZpKkUw7FQ9ANtBS8vI6uGygoGvWR2WGEnl2XD7Vc2yhiIiRgvNqDQTtklKnIs/ipd85bv75vzDyBvOFisXnKC4JUVeDoxzjffvP60hbOzEUxGzN/1ulC56f3qmZQ1ctkTatr4pCi6VobcSuyR2K5/ehkie6xQwowF3ZLm8Wh2Mh/7VwSHMvgoZO+INWKeEgYaIcU2dgRJ9+H751HLMBjZavUM+fg+2+Zw9X1As6t5nry940iW8UKShXN85E9EnEZ1Ul7y5nOXW7RAI+KZux6RhY80GFTOyjrrQO68b5Vz33HEpBVHc9e2ejp92xFUVEREtr/LMhUwGSL/cV2ScVYsPk+Kix21sNWVFQspAs41SBIVTTDmujdyMVUDgcnQl31a5Ie4cUhju2R+1fULzUfOL4g5dOWhY2CJwrNCfvGgpiMiPjROw9Yk4CAmkNqzaVi86KiIi+rXXfPDAqhutBQPRbb03QD313exm0Hx5GImA/9dBf9JhVNp6vUnCddDd7qkAKIQ2q4InuTVSGq09Pweg4lwsjJ6sCnrvWDr51L4a75CYw16cbwMZkIiwPtkFrbNsV4slGKSDx+5r7DAHpX9v++m+fAsQz+6Y0bPfn7RhHiNvB6ZI8IKKM4ac8wDKxslXCgQ1GRDMhodJUVZPodGrWDst4eONWcnbsFqQdOTCIi8fjyd1d7+j1b4dghFWtdCWIYBrI9juxdSuZhGMDp2Zj1a2Rd0qxH6sJaDie7KDQHTGc3gKFxd9tBHGq0C8LDCD27Lh9qWUgXcMQj/VGA2dHyz7/8IP7P73/bjl8PCBziYdE1h1Qya24QGrPxtEHiaorHykmvrudRUDTcdmAc8bApSHWzAVR1o+WI30HTbOFXc0jRc62NiDzrSt9AsXrK1Y/I3u0Hx/E3P3cv7j3afYH3ofhwTNpbzhRxfi3nT9dzwERIxNaAO6QSYREiX7s//Mz9RxAUuJ4VJsfDIu47lsA/vbE6FG4/N1juYjobjZAOqVGctLddqiAvqx0LUiT+nSvXDiY03UCp4oFSc74/peZrWRkCxyBuk8gICBweOTONx99KDqw8v+i01Nw68LbfX6QLChRNb+msDkk8Ch0MXTpfnbDXGNkDYDtpb7tUweVUHrcdGHf8PeqZikoICtxQC1J52fws0i4IDyP07mR8qKCkaFjdLnvKIQWYN876hThhbizgWocUeVDNUC5IidVCb69Ny/lOtdD8toPjmKhOgEp3I0hpuidKzYlDiqZrbcStDqlyHx1SDMPg3qOJtmOgWzGfICeN3ra+f/18CgDwyJkZl6+EfuJhEQVF6/kGrhnJbHnXsyYeFvHnP30XfumREz37Pt936xyubxZxdiXbs79zlLAcUh6P7JENXKcdOMMAERU7FaTIa7ZVqglSJDJEe6l5QOhPqTkZ/MM2cX6//5Y5bBUreO5KuqfftxlFWXXkKLUie00cUgsb7bt4IxLXkUPq/FoOQYGzDriAmrC9vLl7n/PGsrlGvv3QhOPvUQ/DMJhPhDy/bmkFcSv6gtTg8QUpn5YsblYn7HlMkGrG3FgAqy45pEimm3ZBisTVvBbZe315C1GJx9HJMDiWwXhQwGYXHVIVzaDKddRsmo1aFQyp75CquFdq3g9BqhccrNrqlzzukHryXApHJsM4NtVdBGCUmKie+G8NKKa5tl22jb3cf3wSN+2L2fyJ7vjet82C92N7XbOSKSEocJjweOTViuzJ3o8ht2KrqODjf/UyfvN/nMXfvrKMS8mcdR/vtJj+pn0xCByDz7+8ZP0aESRojwz1q9R8LVvGTItai3efnEJY5PDPZwcT2ytWNIQdrCMCAofxkNA0smdNK2+xlwqLnUX2zq/mcHI2ukO8CwgcpqKSbWTvO9e3wDDArQfHHH+PRuYTISx6fN3SCiKoR/wOqYFDz67Lh0osVd9Dkb1WzI0FXROkyMlJq4ctDZDIntccUm8sb+OWA2PWwzneZWdLRdMhUOQ6IrGzxvJQzQsdUoI7HVL9nLLXC4Iih+mo5Gnre7mi4YWraTx0asrtS/EERHDIDCi2l8rtdkj1g/GQiHedmPRje12yslXE/ongnhyXNBCxBKnhdkg9dyWNJ95K4nMvL+HXPv86HvvP38IvfOZVAJ273PaNB/Hhe+bxuZeXcWU9D8A7HTbNnNuNXE7l8Uuffc2xM3Qtay+kEwICh/ecmcFX30xah3L94sm3ktguVZCIOFuzz8YCVndfIwvpAniWaemiC0vOS80NwzAn7M1Ed/3e/vGg7SS815a2cHwqglige/F7PhHG9XSx6RQ/r0MEddojs8OIL0j5tOTahrlhOjzp7ZHEhLnxALZLFatjZpCsbcsIiRz1VlAixnipQ6pc0XBuNYvbDtay8Ymw1FWpuaobVMXgmjqkPNAh5WZkj2Vqrx2NHIqHPN0h9epiBrKq44Hjk25fiieYqPbaDWLSnqLq2MgrA+sr/MCt+7CyVcJr1di0j3NubJU93x8FwHKRDHup+aWUKRy98puP4YlfeRD/6Udvw0funcdH7z/SlcvtF99zHBLP4g8evwig5pCKUB7Zs7ot2zigP//yEr7w+g28cG3T0d+bysq2E/bq+cAts9gsKHj+qrO/sxu+fWkd//Nfv4pb94/hZ+8/7OjPzMQCTSN71zYKOBgPWQe+dpBScyfC/npexmZBwem53YLUgYngrg4pwzDw2vUMbj/UXX8UYT4RgqLpTZ1gXocI6v6UvcFD72rdhwoWNgqYjIhW+aLXmauevLjhkkrmypiNBag/CRU96JA6t5pFRTN2lDXuxSFFk8jTbOHnCYcUz7kiSBUVDUGBo/qzdijhbUHqmSsb4FgGdx/pvtx9lLAGLQzAIVWLhw/Gjfs9b5uByLH40huDm341LKxslbBvCAQpnmMRFDirA2lYuZzK48BEEBGJx4mZKH7kzgP4dz94M37r+2/q6nkzGZHwsXcdxZe+u4o3lrdqkT3KHRpWqXkbh9Sz1a4nJ51PedmcRN3KIQUAD52aRkjk8OU+xfZeuJrGx//qZRydCuO/f/Rux/uf2VigRWSviMOJ1gf7YYmHbuyeqGzH+dUcAODUrJ0gFcLKVgl6nYtpMV1Epljpuj+KcLialhnWHql82S81dwt6dl0+VHItXbBuQMPA3Ji58FvdGrwglcqWMU15XA/wZofU61aheS0bH490J0ipmkFVZK9pqblGHFL0XGsjEs/uihoOgqKiIUj5gv5QPIS1bHlgJde95tkradx2YGxoDiv6DemQygygQ8qKh7fZ2PWKWEDAgyen8KU3VndsgnxaU1RUbBaUjsuwaSUs8VYp8LByKZXH8eneduZ9/F1HEA+L+N2vXLAiW56J7LUQT7aKCs7e2AYAPHe1vSBFBv+0c3YGBA4Pn57GV8+u9Ty2952lLXz0ky9h/3gQn/7YPRi3mfbXjJmxADby8q7DXMMwsJgutO3iJa44J7G9C2umIHV6dncf4IGJICqagVSuFh8kQ3/efnBvDilSoN6sbuA7S1u4sp737HOATDmk/fM3jPiClE9Lrm20v4l6iZpDavCT9tZsph7RiBcje28sb2M6Ku1YyCTCIjJFpeOsu6rrLW3Vg6ZZZE/TdbAMmk6joQGzQ8qdyB6theaEQ/EQDAO2XQ+0kytX8MbyNu7343qOGQ8JYBlgfQBRB9JjMqjIHmBO21vLlvHK9czAvqfXuUEm7A2BQwowYy6dlDJ7DU03cHU9jxM9FqSiAQG/8PBxPH15A199MwmA/g0xwzAQ20Tyn7+6CcMA7j0ax9mVbeTKrcX4VPXe6OTg9gO3zCFdUPDiQm9ie4Zh4PmrafzUf3sBiYiEv/7YvZh02B1FmI0FYBjAem5nj1QqJ6OoaC0n7AFAqHqI5qRS5NxaFtNRyXLe1kME7vrY3mvXMwiJHE7adE51wr7xIASOsRWkLiVz+KE/fAaP/P5TuO3fPY4P//nz+I9fOY+nLq7v6XsOklxZhciztlPaffqL/4r7NCUvq1jPyW1vol5i1qXInmEYSGblgW4QusWK7LkgJHTLd5a3cNvB8R2W+URYhG6Yp3ROMQzDnLJHkSBFio8D+GgAACAASURBVLkbnTQVna5pgHa4F9lTEaS00JwwX7XvX/dgsfkLVzeh6QbeeSzh9qV4BoFjcSgewpX1/kcdSGxkkM+bR2+agcT7sb1OIJOwOi3DppWwxDkuZfYiy5kiZFXHiem9bert+PA9h7B/PIi/e3UZgPla0k6Abz205LkrGwiJHH7+3ceg6QZeXmgtVndy33r41DSCAocvf7f7+42q6Xj2ygZ++wtv4v7/8HX82J8+j7DE468/dk/b2KAds2OmgNUY27Mm7LVJm9QmVTpzSJ2es5+WeqA6xbd+0t5rS1u47cD4niseOJbBwYmQbWTvq2+uAQD+7Q+8DT9w2z5kSyr+7FtX8dN/8aL1GtBOXq74cT2XoHs34+Mq1oS9IRKkJJ7DZEQcuENqu1SBouqY9oAgJfCkQ8obltvtUgVX1wu47cDOUbbx6ulWJ7E94qYSKHId8SwDlrFzSBlU90cBpNR88JG0UkVHkHKH1MGq9X3QPVKKqu855vDMlQ1IPIs79thHMWqcmIniYjLX9++TzJYh8izGuyhZ7paIxOPhU9P40ndXh3YCU68hn30Sg/E6kQ6mhHmRS0mz0PxYjx1SgHnw9CuPnbT+2wubYklofeD07JU03nE4jnuPJCBwDJ5vE9tbsyZRt18nB0UO7zk9ja+cTXZ1v3n2ygbe8TtP4if+7AV89sXruGnfGH73R27FV375QevZ3CnkupMNB95O91LkZ15oM6lS1XRcSuVx2qY/CtjtkCpXNLx1I7vnQnPCfCJk65B64q0kbjs4jp++7zB+54dvwRf/5QP4wi8+AAB4Y9kbAy8KsuaJz94w4gtSPk1ZSDtT9b3G7Fhg4A4pN06su4V0ElV0bzikzq6YHQW3NWTjE1Urc7oDQYqIcDQ5pBiGsXUaqZpBdX8UAIg8i4pmDLxPoKSo1Ef2piISggLXtIuhHxiGgQ/+8TP4jX84u6e/59nL5kYjQLkLjTZOTEdwbaPQ94ERa9vuDND4wK1zWM/JeNHhRK1RZzFdREBgMR2lv1vSCRGJH+ope5fXTUGq1x1ShB++fT9OTEfAMKDe4QuYB07FJgJkKlvGpVQe9x1LIChyuP3gRNseqVRWRlTiHccV33fLLDbyMl7qIrb3e1+9gJDI408+cgde+63H8Oc/fRc+dNdBjO1BxCfrezuHlMixbYcXEFdcu9jrUqYERdWbvg8DAofJiGQ5pN68sQ1VN/bcH0WYT4SxmC7smAaYzJbx+vI2vuemmR1fe3ImAolnrXU67eTKKvVx2WGFnl2XD3UQVf/w5HCc3hHmxoIDLzVPZs1M+aCmHu0FwWORPVLWeOv+nQ9ba6pVJ4JUVYSjqdQcqHYxNUT2NF0HR9l1NiLx5gJr0H1kZMoezTAMg0PxwU7ae+HaJs6uZPH4W2tdi4TrORkXkjncd9yP63XKiZkIVN3o+4SiZLbsyuHHI2fM6Vf/+J2VgX9vL7KYLuJQPET1NNBOiEi8Z6bs5WUV3zifattrVM+lZB7TUQljwf44DzmWwR986O34N+8744n3xK0HxvDUxXXbwRxEfLrvmNkzeO+xBM6ubCPb4vVe2y53NIjhPaenERDYjmN7r13P4LXrW/j4u47gvTfPWd1NeyUeFiFwjLXeJ1zbKOBQItTW0R5xGNm7WhVGj001NwscmAha/ZSvXa8WmvfQIVVQtB2HvU+8ZXafNQpSPMfi9FwMZ1eyPfne/aYgq4j6gpQr+IKUT1OubRQxE5N6drOmhX1jgYFH9pIdWJHdRvRYZO/syjYOJ0K7Tra6cUjROrlOsikPVT3RIdV+Es9e+dq5JP6vL5/bEYUqVTTqI3sAcCgRwvXNwXUrfOaF6wDMSW9vrXa3QCQbjfuP+YXmnUK6Zy5Woz/9IunSRNeQyOO9N8/iS2+senZ65CBZ2iziUHx4HOhhyTul5v/31y7hZz/5Eu74xBP46b94EX/9wqJVqt2My+t5nJjpjzuKcMuBMXz8waN9/R694iP3zCNTrOCfbHrjnr2cxlhQwE37zJ6je4/GoRvASy3ck2sdCukhkcdDJ6fx+JvJHW6ddvzlMwuISjx+5K6Djv+MExiGwXQ0YK33CQsOp5WHrcheO0HKXDMcnWz+Xtw/EbQcUq9d38KBiSCmo73Zf5D+y/qDlSfeSuJwImTr2rp5Xwxnb2x39DNyi7ysIhIYrj2vV6B7N+PjKk5vol5jdiyIbFkd6MKJZMrd2CR0iuWQ8siUvavrBRy3KRmdIA6pfCeClPlvpimyB5gW7MYNnhcie5JAJgT2b3P6X5+6iv/6rav4nv/8LfyLP34Wn395Cbky/aXmACyH1CAWaum8jK+cXcMHbpkDADx9eaOrv+fZyxuIBXjcvH+s/Rf77ODYlBnHudRHQcowjI43dr3kg7cfQE5W8eS5pCvf3ysYhoHrm0VrczcMRCQeOQ9E9lRNx9+/uoK7j8Txs/cfwUK6gN/4h7O4+99/Db/zpbds/4xhGLiSyvel0NyrvPNYAsenI/jUcwu7fu+ZKxu492jccgXdcWgCIs/iuSvNY3upLiZRP3x6CmvZsmORf227jC9/dxUfesfBvnQFzY4FsFZXCaLrBhbTRRxxkDQJVw//C0rr9dLVjQImQoK1xrXjwEQQK5kSdN3Aa9czuL2HfY/z1X3hwobp7s6VK3j2ygYeu2nG1tl38/4x5MoqljbpnyhckP3InlvQtevyoYqFjcJQFZoT9o2TSXuDuzkmc2VMhAQrwkQzVoeUBwQpXTewuFnAYZtFvcCxiAV4bBZkmz9pT6UaYxIpE6SaOaToLzU33+/9nLS3kC7ge982g994/xlkigp+/W/fwHpOpr5DCjBPGssVfdeY6H7wt68sQ9F0/C+PnsDJmQie6VKQMjcaCerfezQSFDkcnAjhUqp/xebZkopyRe9qSlQveOexBGZiEv7hVT+214r1nIxSRRuaQnPAFKRkVad+7fDtyxvYyMv46P1H8G/efwbf/LWH8PivPIiHTk3h089fh2LzvFrLlpGX1b4UmnsVhmHwk/fO4/Xlbby+VCutXtosYjlTsuJ6gHmodvvBcTx/zV6Q0nUDqZzcca3FgyenAABPXUw5+vpPPb8A3TDwM/cd7uj7OGU2ttMhtZotQ1Z1HHawl3LaIXV1PY+jU63fhwcmQlA0HWdvbOPGdrln/VHm3x0EywCL1bqBpy6uo6IZeOymWduvv3mfeXh19gb9PVI5WfVLzV2Crl2XDzVkyxWkC4qjm6jXICfHgyw2X9uWPRHXA7wV2UvmyihXmj/sExGpw8gecUjRtdkOSzy2Szu7FzRdp+46G7Eie31ySBVkFamcjFsPjOPjDx7F13713fj/fu5e/OS98/iBt+/vy/fsJcTe/kffvNJXl5SuG/jsi9dx9+E4TsxE8cDxKbx4bbPjWNXSZhFLmyXcd8zvj+qWkzORvjqkOplU1Q84lsEP3b4fT11cx0a+/0KrVyGbuUND5JByGjlym79/dQXjIQHvOT0NwBRWTs5E8eN3H0Kpolm9lPWQz+wJX5DawQ/fsR8hkcOnnl+0fu3ZK+Zhx/0NPYPvPJbAmzey2C7u7pHaKMhQdaNjIX1uLIjTs1E8dXG97deWFA2feeE6Hj0z0/UkvXbMxAJYy5at57k1Yc9B2oTnWEg8216Q2ijgaJu9GZm0R+KUvZqwB5gHjXNjQSuy98RbScTDIu6ct3dhnZyNgGcZTxSb58sqIhL9h5nDiC9I+dhiFZoPYWSPTLoYZLF5Kte5FdktvBTZI5bhZu/TeFjsrNSc0sjeiekILqzldogW3nBIma9juU8dUmQSKHFyMgyDe44m8Ikfurnp4ogm3nk0gZ+9/zA++ewCfvMfz/ZtGuGzV9JYSBfxE/ccAgA8cCIBWdXxymKmo7+HuKruP+73R3XL8ekorm7kLfG711gTXV1ySAFmbE/VDXzx9RuuXQPtkOma88PkkAo4K2V2k2y5gsffXMMP3LbPOnwj3HskAYapCSr1XEr1d8KeV4kFBPzw7fvxxddvIFNdaz1zOY2pqIRjDS6edx5NwDCAF2xcUilr8E/n9613n5zCS9cybYWc//GdFWSKFXz0gSMdfw+nzI5JKCoactVruUoEqRYF5PVEJL7l5ydXrmA9J7d1SB0kgtTrNyByLN5W7fLqFYcnQ1hMF1HRdHzjfArvOT3ddD0q8RxOzkRx9gbdxeaabqBU0RCR+jO0wKc1dO26fKjhSnWKwzBG9sgD78YgI3vZsicm7AG1CXOeEKTSrSdBdi5ImYKAQJnQc2YuhnRB2RHt0nQDAu2l5kJ/I3tEkPRqDwvDMPit77sJP//uo/j089fxr/7uDWh9EKU+8+IiJkIC3nuzaam/+0gCPMvg25c6i+09cyWN6ajkb8r2wInpCCqaYTlkeg3pK3SrQwoATs1GcdNcDP/wmjuxvU89t7AjQkQj1zeLYBkzWjMsOJ0S5iZffmMVsqrjg3cc2PV7YyEBN+8bw7M2PUeXU3lMhARrWIpPjZ9652HIqo7PvbwEwzDw7JU07juW2NUn9PZD45B4Fs9f3V1sTnqXuhWkFE1v2U9lGAb+8plrODMXwz1H4h1/D6eQ6yf34YWNAgICixmHheLtBgNYheZtBK794+Z95cZ2GWf2xXpeF3IoHsZiuoAXr20iW1Z3Tddr5Ob9Mby5QnexOblvhX2HlCvQvZvxcY1XFjMIi1zLsaJeReRZTEakHcWD/UTVzI4YNzcInVBzSNH74CAspAsQORZzY0Hb30+ERWx0VGpenbJHmUPqpjnzdOvNusloFc07Dql+RfYsQdLDTk6GYfCv33sav/zICXz+lWX86ue+01P3TCpXxuNvJvEjdx5AoCoQRiQedxya6KhHyjAMPHdlw3aj4eOckzNmKfKlZH96pIhDyu0BGh+8Yz/eWN7G5VR/Jwo2sp6T8Zv/+Cb+n69fHuj37ZTr6QLmxoK7XDpeJuKByN7fvbqMY1Nh3HbAfijDfccTeO16BkVl57/hciqHE9NR/95nw6nZKO4+EsenX1jEhWQOG3nZdgqrxHO4c37CmtRaj+Xs7GKdfOfhCYRErmVs75nLaVxM5vHR+w/39WdIrp/8exY2zOFQrMO1WljikZebr5eubpj303Z7s6DIYTJiiqe397A/inA4EUKmWMHfvbKMgMDiXSemWn79zfvHkC4o1utCI+S+FfWn7LnC8DwJfXrKS9cyuGN+grqNea/YNx7AjQEJUumCAt0Apj0nSHnAIbVRwKFEqKkwk4iIyBQVx1Goik5nh9TpqiB1rk6Q8kKHlGgJUv1ySBUwHZU8PxWFYRj8ymMn8evfe+r/Z++sw+O4rj783l2hhbYkW7JlWzIzM9R2GBtmpiYNFL40KSRtU0iTNmmoSRrGNsyMjR0yxnbsmEm2ZRLZQgv3fn/cGWktC3ZES+d9nvtod2Z2NPe3s3dmzj3A2yt3c+Or33fYvl9dlkutR3PelH6HLJ85KJUfdhfXh1m0xsZ9ZRSUVTNDwvXaxcCe5kGis/JI7S0JjAIaPx7XG5eCN1fkdun//dJ6KF24pSCgr2HbiypCKqE5NOSQCtRKe9sLy1mas5/TJ2Q2a5SYMTCVmjrNspyGcGatNZvyyiSheQtcNK0/O4sOcueH6wGTL6oppg1IYd2eksOuO3kllbgU9UYUJ0RHuJkxMIX5G/Oa9cB5+pttpMRFcfLY3o737wQ7VNqe8N7msFp5fLT7MGOoN1vzy3G7FP16tL7PPpb3ZUfmj7KxvdLfXbWbWYPSiG2liMxIK7H56tzAzSPV4CEV3PeTwUpoWhuEdlFcUcOGfaVMyeo8t1Z/k5EUw54DXROyt8/PSWad4nYpXCo4DFLbCyuarLBn0yMumjqPpqTy8CSaTVFbH7IXWENjUmwkfZJjWbenwasimHJINVW1qCPIKSwPqcIL180bxNU/GsDbK3ezuwPGpzormfmMgSmH5ZyYNTgVrWkyPKUp7Ad9yR/VPrpFRZDZPZaNneQ51JbS6Z1Bz4QYZg9O460VuzstN1pTfLHBVNsqr65jxY7ADdvbUVgRtKHGzWF7FpS34OHhT95csQul4LTxzRe8mJzVnUi3OmRcLCyv5kBFjSQ0b4FjR6aTlhDN/A359O0R22zScNtQtXjboWF7e0sqSY2PbvMk+JwhaewsOkhO4eGh0Jv2lfL5+jwumNqv3ku4s6gP2SuppLbOw86iCkf3KN2iWg/Z69vdN89KO7H5hH4dn0+zv2Vkq6nTrYbrgfHydykCOo+UbZCSKnv+IbCeuoSAYNl2c6GY3Ilx1v4mIym2y0L29gZATg+nRLhdAR+yp7Ump7C8/sLYFHa+B18r7dmhUpEB6Hk0PCOxkYeUJiLgDVKdm0NqW0HLBslg5IyJJrfJlz5UDWqNLzflk7v/YH0yc2/GZiYRHx3B162E7Xk8mkcWbOHvH61nTGYSfZKbDo8VfGdIr4RODdnzZ0Jzb06f0IddBw4e9vDZWdTWefhyYz7HjUzH7VJ8tan9v6HOoKyqlsLy6pCqsAeQHGuSAeeXBl5YjtaaN5bvYsbAlPrCNk3RLSqC8X27H5LYvL7CXi8xSDVHVISr3gu3qXA9m7GZycREuljUKGxvb0lVu8atOUNMxcQFlkHam799sI6E6AgumZHV5v37Skykm+RukewtqWTXgYPU1Gmym8lx2hStJTXfkl/WakJzmylZPRjVJ7HeMNWR2MZ0peCI4T1b3T42ys2gnvGsCeBKe2WVErLnT8QgJRzGkpwiIt2KcZ0QdxwoZCTFUFpVS6mPnjPtYV+pXT0kOJKaA0S5XQHvIbWvpIrKGk+Ls089LIOUr4nNazyBmUMKYETvRLbml1FZY2afg8lDqqqm42fMy6pqKSirCikPKTBJrzOSYpi/of0P0/9dtIPU+CiOGZF+2LoIt4tpA1JazCOVV1rJJU8v4c4P13P0iF48f/nUdh+TYL7jrQXlnVJpb29x4OQrPGZEOnFR7i4L21u+4wAllbWcMq434/om86XDpP1dxY76CnuhNXalJUSTGh/F6l2B5wWxbPt+dhRVcPr4w5OZN2b6wBR+2FVMcYW5P9ycLxX2fOH8Kf3omRDN8aMzmt0mKsLFpP49+O/i7Rxx93zOe2wRv3x5JWt2FbfLs7NfSjcGpMYdlkfqy435fLEhn+uPGERKfNfcg6cnxrC3uIptdoW9VN/Pm7hod7Mehh6PmYQd4OM9zyUzsnjvhtmdkjOrW1QEPROimdivO6k+6jqqdxI/7A5cg1S5hOz5lcB76hL8ztJtRYzuk9Tprq3+pHGcd2eSV1KJ26W67GLYEUS6VcAbpBoSWrcUsmd5SPmY2LymNnA9pEZkJODRsGGv8ayo8+j6fF+BSnRk5+WQyrFv9oI4oXlTKKWYMySNbza3LwdO7v4K/rd+H+dO7tese//swansKKqof0D2ZsHGfE64/yuWbCvib6eN5uELJpDUTcohdwSDesZTXethRwdX2qup81BYXhUQIXtgZsWPH53BB6v3srOTqgp688WGPCJcipmDU5k9OJVVuQc4UOF7UYumWJZTRGFZVesbOmBHkRm7Qi1kTynFmMxkVuUGXqjkG8tz6Rblrq802hIzBqbg0bB4m/Hi2byvlPjoiIAx9AYq6UkxLLnlKOYMaTnB9W+OH8ZF07IYnpFIdZ2HJduKKK2qZWL/9oWW/WhIGgu3FtZP2tV5NLe/v46+PWK5dGZWu/bthF6JMewrqay/R2muCnRTtFRlb3fxQSprPD57SHU2/zx7LH85dZTP24/sk8S+kiryAtCDEqBUQvb8SmA/zQhdTmVNHat3FYd0uB5Q77LdFYnN9xZXkhYfHfDeLN5EBoGHVP3FvqWQvXhnHlK1dlLzAMshBSZkDxoSm9fWeQL+nOrMkL16g2SIeUiByYdRWlXbrhw4LyzeAcB5TYTr2dj5oLzD9mrrPPzjo/Vc8tQSUuKiefeGWZw/tZ9Ul+pA6ivtdXAeqbzSKrQmYEL2AC6fmY1ScPKDX7dYBasj+GJ9HpOyupMYE8nswWmOcqQ1RV5JJec8toj7P9/UgUdpch8CzebZCWbGZCaxOb+sxbCjzsbj0eSVVrI6t5jP1u7jP4u2896qPRw3Kt0n74fx/boTE+mqP3c255uE5jIGdgyj+iTxh5NH8NAFE3j9pzP45jdHsOEvx3HNnIHt2u+coWlU1hgDF8DLS3eyYV8pvz1+eJcWeUhPjGFvSSU5hRXERblJczAZ3TPBRG8UNGEE35pve1wFxj3P7MFp9felvjCqt1UtOkDzSNkhe2KQ8g+B99Ql+JUVOw5QU6dDOqE5mJA9oEsSm+8rrQqqcD2wDVKBnUMqp7CCKLerxXwQDSF7vs1w230ORA+pvt27ERflbjBIBUUOKdtDquND9myDZKh5GQDMHJyK26VYsPHwfBi+UFVbx8tLd3LU8F4t5nwamBZHemJMfdhefmkVFz25hIfnb+HcyX15+/qZ9cYToeOwq3Vt7mCDVCDmKxzRO5F3r59FemIMlz69hAc+39QpSc73FB9k/d5S5g01+UzGZiaREBPRrjxSb67YRZ1Hs7AdRq2m2F5UQXK3SJJiQ8/jcGxmMlrDD37KFXOgopqj713AlNs/5+QHv+bK55Zx61s/UFXr4YIWjPPeREW4mJzVoz6P1KZ9ZZLQvJPpCGPftOwUoiJcLNiYT2llDfd8uoHJWd053gevuI6kV1IMBWVVbM4rIys1zlHfZlmTRE2NW1ut0NGBaYFhkHLKCNsgFaB5pCRkz7+I6sIhLMspQimY1D+0DVK9EmNQCvZ0UchesM2EBkXIXkE5fXvEtuglFB3hJj46wvek5raHVACGwrlciuEZiay1DFJ1QZVDqjM8pCrolRhNt6jQu4wlxkQysV93FmzM56Zjhzn+/Ier91JYXs1F0/u3uJ1SilmDU/ls3T4WbS3kZy+uoKSyhrvPGsuZE1vPtSK0jfjoCPokx7KxicTmC7cU8u6q3Vw5K9txaIZd0bVngE2AZKXG8ea1M/ndm6u559ONrNx5gL+eOoraOk3xwRoOHKw2fytqKD5YQ4n1uqbOw0/mDGBYeuuz8F+sNw9wRwwzBqkIt4uZA1P5cmMBWmvHD7xaa177zuS+2pRXRmFZVYeF3e8orKB/kN0T+MqYTFPefVXuAaYNSGnXvjwejcvBNU5rzc2vrWJHUQW3nDCcfindSE+MIT0phlSHXuozBqby94/WsyW/jLzSKjFIBQGxUW6mZvdgwcZ8oiNcFJRV8+Qlk7vcsy09MQatYfmO/cwb1nrCb29G9k4kJS6KBRvyOa1RvrOtBeXER0eQlhBY47uvJMREkp0axw8BmGMOTF7S6AhXwKfCCFVC705eaBdLcooY2ish5HOFRLpdpMVHs6e48z2k9pZUMjnIPM6CImSvsLzFcD2bHnFRvic1tzykAtXzaHhGIm+t2IXW2vKQCuwLp1KKqAhXp+WQ8uX7D1bmDE3jro83kF9a5fgG9LmFOQxIjWux2pHNrEGpvPZdLuc+tojs1DievXyKIzd8oW0M7hVfX73LpqbOw2/fWEVOYQUvL93JeVP68rMjB9MzwTePp0D0kLKJjXJzz9ljmdAvmT+/t5YZd/6v2W2jIlwkx0ZysLqOT9fu47GLJ9WXi2+OLzbk0Sc59pDE07OHpPLRmr1sKyh3bNxblVvMprwyzpyYyWvf5bI0p4jjRjWfrNkJO4oqGBuiRWNS4qPpkxzL97nt84JYvLWQq55bRp1H0ysphl4JxrA0uFc8V8zKbjIE67mF2/lk7T5uPXE4V84e0K7/P3OQOd+eX7gdkITmwcKcIWn89f11bC8s57TxffzyO0tPMtfriuo6xzkuXS7Fj4aksWBj/mEG2a355QxIc+ZxFWiM7J3Iyp2Bl2MOjEFKKuz5j8B+mhG6lNo6D8u37w8640lbyUiO7XQPqcqaOg5U1ARlyF51beCG7Gmt2V5YQX8fDVK+JjWvtQxSzSWB9jfDMxIpraold/9B6jyaiAAMLWxMdISrc0L2CssDJpdCZ2Anhv3SYd6dH3YVs3zHAS6Y1t8n74JZg1NJiI7g+FHpvH39TDFGdRGDe8azJb+MOq/wtde+yyWnsIJ/nDGGC6b246UlO5nzj/nc88kGnyrC7iutJMrtqg9VDjSUUlw0PYs3r53J708awV1njuHxiyfxytXT+eSXP2Lx745k/V+OY+Nfj2fJLUfx8S9/RHpSDJc8tYT3Vu1udr9VtXV8s7mAecPSDnlYmz3I/Ia+akO1vde+yyU6wsVvjx9mlakvct7hJqip87DrwMGQ9ZACGNs3qV2JzTfnlfGT578jNSGasyf3ZXh6Q/Lrf3y0gfMfX3xYjp01u4u5/f11HDGsJ1fMym5vFxjZ24R82l5yg3tK6HIwMNcK2XW7FDcdO9Qvx+BdVKIt9yhzhqRRVF59WEW6rfllPlfYC1RG9Ukid//Bdheb6AzKqmolf5QfEeVDiIVbCrnjw3U8f/nUNnk4rdtTSnl1HZOy2lfpIljISIypL+fbWeSXmpumQKl65CuRblUfvhaI5JVWcbCmjmwfqpekxEX5nLy+Ial5YBp6hmeYm+K1e0qoCYKk5mAMUtUd7CFVWllDQVm1TwbJYGVERiKp8VEs2JjPGQ7C5/6zaDsxkS6fQ+5S46NZ9vujujTpq2AecKtqPewsqiArNY7Kmjoe+HwT4/slc9akTM5Wfbl8ZjZ3f7KBB/63mY/X7OOVa6a3mHdoX3ElPROjA34GfVSfJEb1SWp1u97Jsbx6zXSuem4ZN7y4grySKi5vwtiwdNt+Kqrr6vNH2fRL6Ub/lG58tSmfS2Zk+Xx8VbV1vPP9bo4dmU5KfDQT+3dn8baOMUjtPmAmE/qFsEFqTGYyH6zeS1F5tWPjaH5pFZc+vYRIt+LZy6Yclu7gg9V7+L9XVnLKw8bNLQAAIABJREFUg9/wxCWTGJ6RSHlVLTe8sILucZHcfdbYDjn/3S7FtAEpfLp2H9ERLvp0bz4XnxA4DEyLY9agVOYOTWsxv2hn4u2h2paiK7MHp6IULNiQz5hM4+FVUV3L7uLKgKmw11ZG9Tbj/prdJfVFVQKB8qpa1u4uITEE8/oFC4HpBiC0ieRukazKLeY/i7e36fNLcswN15QQr7Bnk5Ecw54DB9G68zyB9lo5PYLPIBXYIXsNCa1bv9inxEf5nNTcNpwEYg4pgKHpCShlKu3VBUFSczB5vDo6ZM+uUuWLQTJYsV33v9qUf4gXTUsUH6zhrZW7OHVcH0cJk8UY1fUM7mUeLOxKey8s3sGe4kpuOmZo/QN1VmocD54/gWcum8zWgjJ+8tyy+pLmTbG3pDIgw/XaQ3K3KJ6/YirHjkjnz++t5fb311Lb6Nr0xYY8oiJcTYb1zR6cysIthY6M4p+vy6P4YE29UXdqdgrr95ZQXNG6l1pr2GNXvxAsxmDjnUfKCQer67jyuWUUlFXx5CWTm8y9ecLoDF69ega1Hg9n/PtbPl27j9+/9QM5heXcf+74DvUOnGmdTwPT4oNi8kcwXpj/uXJqu0M220OPuCiirHvItnhIpcRHM6ZPEvO9vKNzCsy4MSBIE5rbjLQSm68OoMTmlTV1XPXcMrbkl3HdvEH+PpywJTCfuoQ2MTwjkTlD0nj6m20t3rQ2x7KcIjK7x5KRFB4zQQPS4imvruvw0tve7Atmg1QAh+zlFPpe/rZHXDRF5dU+GR5rPYFbZQ+gW1QE2alxrN1dQq1H4w7wHFJgh+x1rEFqm2WQbMvsYzAxZ0ga+ytqfL55e+27XCprPK0mMxf8j52TZlNeKeVVtTz0xWZmDkphRhOzxnOH9uTus8ayeFsRN77yfbNV6vaVVNErKbiuNb4QE+nmoQsmcPH0/jz+1TZO//e3hySE/2J9HtMGpDRZ4GD24DTKq+tYsWO/z//vte9ySU+MqZ/Bn5rdA60bJu3aw/Yi82AZitVBbUb3SUIpk4fLV+o8mp+/tIJVuQd44NzxLeb+GZ2ZxDvXz2JQz3iuem4Zb6zYxc+OHNzuJOqNsX+LtvFYEHxBKUXPxGgSYyLo3sZ8vHOGpLFix/56I/jWAvOcMiA1uM/F7nFR9EmO9VsVzsZU13r46X++Y+HWQv559liOHdm1FRmFBgL/aUZwxDVzBlJQVs3ry3MdfU5rzdKcIqaESf4ogONHpeN2Kd5YvqvT/se+EuOZE2yz1pERLqoD2UOqsIJItyLDh4evlLgoauo0pVZJ15awZ94DOVn48IxE1u0NHg+pqAgXVW0wkLdEvYdcj9A2SM0enFbvut8aHo/mP4u2M6FfMiN7tx4OJfiXhJhIMpJi2LSvjGe+zaGwvJpfHdN8zpNTxvXhdycM4/3Ve/jze2sPM7BrrdlbHHoeUjZul+LPp4ziofMnkLv/ICc98DUPz9/MlvwythaUc8TQtCY/N31gCm6X8jmPVF5JJQs25nPahD71XjFj+yYTFeFiybbCdvdjZ1EFUREuevmYqD4YSYiJZEBqnM8eUlpr/vLeWj5Zu48/nDSCY3x4KOyVGMPLP5nOWRMzOXF0BjccMbi9h30Yg3vGM29oGseMkIdUwRmZ3U2BhbaGj84ZmoZHw9ebzbi1Nd/3SdhAZ0xmEl9vLmBz3uFVZruS2joPP3txBV9syOf2U0cfVtVQ6FoC96lLaBPTBvRgbGYSj3+51ecwDzAeBwVl1UwOk3A9MLlT5g5J4+2Vuxxp5YR9JZVER7hIjA2udG2RrsDOIZVTUE7f7t18Cq2zXfiLfEhsblfZC1QPKTC5hXYWHaSssjY4kppHdnzI3rbCctITY4iNCu1Qsx5xUYzJTGb+xrwWt9Na8/hXW9lWUM7F07O65uCEdjO4VwIrdx7g0QVbOGp4L8b3azl/41WzB3D5zGye+TaHx77cesi6kspaDtbUBV0BDaecOCaDT375I44a0ZN/fLSBM/79LdCQzLgxiTGRjO+bzFebfCsO8JZ1P3DGhIaHk5hIN+P6JndIHqntheX069HNp4IDwczYzGS+zy1u1TNZa83dn2zgmW9zuHxmNpfN9D0heWyUm7vOGstDF0zolJA6pRRPXzaFE8d0THVFIXz422mjueussW3+/NjMZBJjIlhgXfu35pfRJzk2JO55fnn0ECJcLs5+dJHfPKXqPJobX/2ej9bs5Q8njeD8qf38chxCA2KQCjGUUlwzZyA5hRV8vGavz59barmih0uFPZvTJvRhT3Eli7a2f+azKfaVVNIrMSbgk8w2JvBD9ip8DtfqEW8MUoXlrRukaj0mUXggf192YvPqOk9QeEh1RpW97YUVZIVw/ihv5g5J4/udB9jfzPlbU+fhlrd+4I4P13PMiF7y8BREDO4Zz7aCckqrarnxmCGtbq+U4tYTh3PSmAzu+HA9lz+zlAf/t4lvNxew1SrQEWzh4W0hNT6ahy+YyIPnj0cBw9ITWrwezB6cxqpdxc3+hmy01rz+3S7G90uuD6m0mZbdgx92FftU7bAlthdWhHRCc5sxmUnkl1bV59Fsjgc+38xDX2zhvCl9ufXE4V10dILQuQxIi2dgOxKQR7hdzB6cxoKN+Wit2VpQHvT5o2yG9Erg1WumExvp5rzHFrHER0O/x6PbPf5qrflyYz6nPfwNb6/czU3HDm2yUIbQ9YhBKgQ5ZmQ6WSndeHTBFp8Tdi/Ztp+UuCgGhsiA5ytHDe9FQkyE4xBHXwnWEIrIiMBNaq61Znthuc85OFJsDylfDFJ1OqC9owBGZDSEY4VrDqmcgvKQcF33hcau+94UV9Rw6dNLeGHxDn46dyCPXDiRyABNyC8czmDL6HHSmN4Mz0j06TMul+KfZ4/lsplZbC8s5+5PNnL+E4s57WHjKRSM15u2ctKY3nx58zxevGpai9vNGZqG1vD2ypbD83/YVcKGfaVNVqicOiAFj4Zl233PRdUYrTU7isLEIGXlgPp+Z/MeEA99sZl7P9vImRMzuf3U0SHvNSYITpgzJI19JVWs31vK1vxyBoTQPU92ahyvXjOdtMRoLn5qMfM3NO0FXl3r4cuN+dz61mqm3/k5k/76GZv2tS3Ub1lOEec+toiLn1pCYVk195w9VpKYBxDBFUck+ITbpbjqRwO45c0fWLi1kBkDWy+tuTSniElZ3QPaM6QziIl0c9KYDN5euZu/nlrbZFLU9pBXWlVfVSKYiHSrTsshpbXmpaU7qa71OCrFbZNfWkVFdZ3PBon6kD0fKu3V1GkiA9zI0ysxmu7dItlfURMkHlJuCn0Il/SVksoaCsuryfKhwmIoMDYzmaTYSP7x8XoWbi1keHoCQ9MT6Rbl5mcvrmDn/gruPmtskw/RQmAzc1Aq4/sl8ysfvKO8iY5w88eTRwLGKLky9wDLt+8nr7SyxWTQoUhCTOtJg8dmJjE1uwf/+t9mzpzUl/jopq/zLy3dQVSEi5PG9D5s3YR+3Yl0KxZvLWJeM+GBrVFQVk1FdV1IJzS3GZGRSIRLsSr3AMeNOjwH0+NfbuWujzdwyrje/P2MMWKMEoRG/GiIyYv36rJcyqpqGdAOj6tApHdyLK9ePZ2Ln1rCVc8t47hRGXjPB1dU17FwSyGlVbXERrqZMySNb7cU8Md31vDfK6f6/LxaWFbFza+t4vP1eaTGR/OnH4/k3Cl9pbpwgCEGqRDljAmZ3PvpRh5dsLVVg9T8DXnsKKpok3EgFDhtfCYvLtnJx2v2dmhSO601+0oqOWJY225e/Umky0VtXceH7BUfrOHXr63iozV7cSk4YljPJks7t0ROoV2lyDeDREqcyalS4FMOKU/A52VSSjE8I5FvtxQGRSnq6MiODdmrT2geJgYpt0txywnDee27XN77fjcvLG5Izt8jLooXrpoWdqHWoULfHt1489qZ7dpHUrdI5gxJY86QppN6C2bM/O0Jwzn1oW94bMEW/q+J5PHf7zzAi0t2cM7kfiTFHm7kio1yMyYzuV2JzXeEQYU9m5hIN0PTE5qstPfC4h3c/sE6ThydwT/PGhsU1zFB6GrSk2IYlp7Aq8t2AoRMyJ43KfHRvPiTafzqle8PK4LgVooTRmdwzMhezByUSkykm+cX5vD7t9fw/uo9TU4cNGZP8UEueGIxuw8c5ObjhnLpjKwOdzwQOgb5VkKUmEg3l83M5q6PN7B2dwkjmvHS+WFXMdf+dzkjeydyzuS+XXyUgcGk/t3p2yOWN5bv6lCDVGlVLRXVdUEZQhEZoTo8ZG9V7gGue2E5ew5UcsMRg/j3/C089c22+pl+X7ENEtk+GiRio9zERroPC9n7fucB3liey29PGE5MpJkpqfV4fEqU7m9sg1RweEh1bMiebZAMl5A9gLMn9+XsyX3RWrOnuJINe0vJKSzn6BG9yOwe+g+3gtBexvVN5qQxGTz+1TYunNafnl7X5araOm567Xt6JsTwm+OHNbuPKdk9ePzLrVRUt82bekeRuXaFQ8gewJjMZN5ftRutdb03w4od+/njOz8wd2ga9507Liiut4LgL+YMTePRBaaARah5SNkkxkTy2MWTfNr2/Kn9eWnpTv763jrmDe1JXDPermCKdV34xGJKDtbw3OVTmRJGRbuCEbkShDAXTu1PXJSbBz7fVF/O3pudRRVc9sxSuneL4ulLJzfrxh7quFyK08Zn8vXmAvYWt5yA0wl5VjLPnkFY9SjS7eqwkD2tNc98s40z/v0tdXWal6+ezo3HDOXksb15ZelOig86S1KYU1hOhEvRO9l3Q1+PuKhDDFK1dR5+9er3PLtwO797c3V9rjUTshf4Rp4RVr6ZYLiZj45wUd2RBql6D6nweKjzRilF7+RY5g3ryWUzs8UYJQgOuOnYodR6PNz72aZDlj/4v81s3FfGHaePbtI7ymZqdg9qPZrl2w80u01LbC+sQCnC5nc7NjOJksra+kmEAxXVXP/CCnolxnD/OeMl350gtILt+RoT6SIjCCe3Oxq3S/HnU0axt6SSB/63qdnt1u8t4axHFnKwpo4XfzJNjFFBgFwNQpikbpFcMSubj9bs5cQHvubrTQ1JcQ9UVHPp00uoqqnj2csnHzJbGI6cNr4PWpuSzx3FvhKTsygYPaSi3B0TsldRXcvPXlrJbe+uZfbgNN7/2Wwm9jelza+cnU15dR0vLtnhaJ85heX07dHNkTEmJT7qkCp7Ly/byaa8MmYPTuWN5bt46pscwBiqgsHIYydADg4PKXfHekgVlNM7Kabeq00QBMEX+qfEccHU/ryybCeb80xi3B92FfPw/C2cPqEP81oJr5+U1QO3S7G4jWF7OworSE8Mn7FrTKbJZ7Yq9wAej+bGV74nr7SSh86fQFK31nN/CUK4M6l/D7pFuclOjZc8axYT+3fnrImZPPnVNjbnlR22fvmO/Zzz6CIiXIpXrp7OqD5JTexFCDQC/8lLaBe/PHoIj1w4gYqaWi58cjFXPruMDXtLueq5ZewsOsjjF09iUM8Efx+m38lOjWNCv2TeWJ7rc2XC1rC9rYKxDHeEu/0hezsKKzj94W95b5UprfrExZPobiUYBxjZO4kZA1N45pscRx40OQUVZDn0jkmJi6pPal5WVcu9n25kSlYPnr1sCseO7MXfPljHN5sLqPHogM8hBTC4VzzHjOjFBMu4F8iYkL2OyyG1rbA8bPJHCYLQsdxwxCC6Rbr5+0cbqK41nrIpcVH88aTWQ8fjoyMY1TuRxVt9K1PemO1hUmHPZkiveGIiXXy/s5jHvtrK5+vzuPXEEWGXeF8Q2kpUhIvr5g3ivCnhmVKlOX59/DC6Rbm57Z01aK3xeDRfbMjj8meWcsa/v6V7t0hevWY6g3qGZphjKCIGqRBHKcVxozL49JdzuPm4oSzcUsCx933J0pz9/PPssUwdkOLvQwwYTp+QycZ9ZazZXdIh+9tXGrwGqUi3i1qPGeTbwoKN+Zz84NfsKa7kmcumcN28QU3O7lw1ewB7Syp5f/Vun/artSanDQaJHnHRFFlJzR+Zv4WCsmp+d+Jwq4T6OAakxnHdC8vZWVRBVBB4SEW6XTx28aR6b7NAxs4h1VGG3u2FFWSFUf4oQRA6jpT4aK6ZO5BP1+7j+heWs35vKbefNtpnj50p2T1YufMAlTXOjOxF5dXkFJSHVahxhNvFyN5JfLB6D3d9vIETx2Rw8fT+/j4sQQgqrps3iIunZ/n7MAKK1PhofnXsUL7eXMBvXl/N3Lvnc9nTS1mVW8wN8wbxxrUzHRdMEvxL4D95OUApNUIp9blSqkIptVsp9WelVHj4RrdCTKSba+cO4oub5nLpjCzuPH00J49tvUJBOHHSmAyi3C7+u3hHhzw855VUkRgTQWxU8J2Cdm6HGo8zLymtNf+ev4XLnl5CRlIM714/q8XqT3OGpDGoZzyPf7nNJ83zy6qoqK5znNDaDtnbfeAgj3+1lR+P7c04a5Y2PjqCxy+ehMejWZVbHBQeUsFEdKQbrU1+rvZSfLCGovJqslPlRkMQhLZx+cxseiVG88nafZw6rjdHj+jl82enD0yhus7DvZ9upK6FCRuPR/P9zgPc/9kmTn3oGyb+9VMKy6sZ2Tu8wkfGZiazt6SSfj26cefpo30u1S4IgtASF0ztz8jeiby8bCfpiTH867zxfPubI/i/Y4bSwysaQwgOQiaLtVKqO/AZsBY4BRgI/BNjdLvVj4cWUPRMiOG2HzurahYuJHeL4rTxfXhxyQ7Kqmr566mjmkxwumZ3MR+u3suswalMze7R5A2W1prc/QeD0jsKINIyytTWaXzNdV9T5+GWN1fzyrJcThqTwT/OHNNqJSKXS3HlrGx+88ZqFm4pZMag1Ba3zyloW9nsHnFRVNV6+PO7a9HAzccdWvY7KzWOf50/gcueXkKEK6Ts9H4nOsLoWVVbR1RE09rW1HlYsCGfrNS4Fl2s7YTmWRKyJwhCG4mNcvOnH4/iia+2Oq7yOmdIT86Z1JdHv9zK2j0l3H/u+EMefrTWfLYujzs+XMfW/HKUMkaZnx85mLlDezI2M7wMUnOGpvHWyl08dP4EEmIkb5QgCB2D26V4/oqp7K+oZmCIViAMJ0LGIAVcA8QCp2utS4BPlVKJwG1KqX9YywShRf52+mj6pXTjnk83snz7fu4/dxyTskx1hs15Zdz72UbeX7UHgAe/2Myw9AQum5nFKeP6EBPpZl9JJW+u2MXr3+WyKa/M0cxrIFHvIeWVR6r4YA0Hq+tITzrcyFZeVct1Lyxn/oZ8fnbkYH551GCfZ0JPHd+Huz/ZwONfbW3SIKW1ZnNeGUtyivhgtdHeqYeU/cDw0Zq9XDNnYJNVjuYMSePec8bhkhncDsU2SK3bU8qk/t0PCd0sr6rlpaU7efKrrewuriTK7eLXxw/jshlZh4V4aq35erMpzCAhe4IgtIfjRqVz3Kh0x59zuxR/P3MM4/sl84d31nDSA1/x8IUTGdc3mdW5xdz+wVoWbS1iQFocd581lnlD00iJD75Kux3FnCFpfHfrUeIZJQhCh9MjLkq8oUKEUDJIHQ983Mjw9BLwd2AO8K5fjkoIKtwuxXXzBjFjYAo/f2klZz+6kJ/OHci+kireWJ5LTKSb6+cN4qLp/flifR7PfJvDr19fzR0frmdYegJLthXh0aYKxN9OG83JYzP83aU2YRukqus8bC8s56mvt/HKslwO1tRxxLCeXDErmxkDU1BKkV9axeXPLGXN7mLuOH00503p5+h/xUS6uWhaFvd+tpGP1+zF49HsOnCQ3Qcq2VFUzvIdByiyKuSlxkdz9qRM+josm51iXbB6xEVx7byBzW53yrg+jvYrtE6mFcd/9qML6d4tkqnZKUwb0IPC8mqeW7id4oM1TMnuwa0njeCN5bn85b21zN+Qxz/PGltf/fO77UX87YP1fLd9P6P7JDk2SAqCIHQk507px8jeSVzzn+84+5GFzByUwhcb8kmJi+Ivp47i3Ml966+j4Y4YowRBEISWUB2VaNbfKKXygIe11rc1Wl4O3Ka1vquFz86fM2fOnPnz53fuQQpBRWllDX98Zw1vLN9FVISLi6b156dzB5LqNduptWbxtiKe+SaHzfllHD8qndMnZAb9A/MrS3dy8+urmDkohW+3FBLhUpwyrg+9k2N5YfF2CsqqGZaewLmT+/LkN9soKK3mwfPHc+TwtnmEFZVXM+POz6msafDI6hblpk9yLGMyk5mS3Z0p2SlkpXRr083t+r0lHHffV/zllJFcJMkhu5zdBw6ycEshi7YWsmhbITuLDqIUHDOiF1fPGciEfiY5u9aa/y7ewV/fX0tspJtfHzeMLzbk8fGafaQlRPPLo4Zw9qRMIuRBTxCEAGB/eTW/eHklC7cWcsWsbH46dyCJEpomCIIgCIcwd+5cFixYsEBrPbfxulAySNUAN2mt72u0PBd4Tmv9uxY+KwYpoVkWbS0kKyWuyVC1UOX9VXu47oXlJMVGcsHUflwyI6s+H1ZlTR3vfL+bp77exvq9pfSIi+KpSyfXJwlvKyt3HqCgtIreybH0SY4lMTaiQ2dWN+4rZXDPeJmtDQBy95tcYE2FToIJj/35SytYs7uEuCg3V88ZyJWzs1vNSSYIgtDVaK05WFMn45MgCIIgNEM4GaR+pbW+v9HyXcAzWutbmvjMT4CfAEOHDBkSv2HDhq45WEEIcKpq6/h6UwHTB6Y0e5OttWbZ9v30SY6ld3JsFx+hEOpU13r48Ic9zByUeohXoiAIgiAIgiAIwUNLBqlQinvYDzTlopEEHGjqA1rrx7TWk4DvMjKCM9ePIHQG0RFujhzeq8UZX6UUk7N6iDFK6BSiIlycMq6PGKMEQRAEQRAEIUQJJYPUemCY9wKlVF8gzlonCIIgCIIgCIIgCIIgBAChZJD6EDhWKZXgtewc4CCwwD+HJAiCIAiCIAiCIAiCIDQmlAxSjwBVwBtKqaOs/FC3AfdorUv8emSCIAiCIAiCIAiCIAhCPSFTEkRrvV8pdSTwIPAuJm/UvRijlCAIgiAIgiAIgiAIghAghIxBCkBrvRY4wt/HIQiCIAiCIAiCIAiCIDRPKIXsCYIgCIIgCIIgCIIgCEGAGKQEQRAEQRAEQRAEQRCELkUMUoIgCIIgCIIgCIIgCEKXIgYpQRAEQRAEQRAEQRAEoUsRg5QgCIIgCIIgCIIgCILQpYhBShAEQRAEQRAEQRAEQehSxCAlCIIgCIIgCIIgCIIgdClikBIEQRAEQRAEQRAEQRC6FDFICYIgCIIgCIIgCIIgCF2KGKQEQRAEQRAEQRAEQRCELkUMUoIgCIIgCIIgCIIgCEKXIgYpQRAEQRAEQRAEQRAEoUsRg5QgCIIgCIIgCIIgCILQpYhBShAEQRAEQRAEQRAEQehSxCAlCIIgCIIgCIIgCIIgdClikBIEQRAEQRAEQRAEQRC6FKW19vcx+B2lVG5SUlKfcePG+ftQBEEQBEEQBEEQBEEQQoKVK1dSXFy8S2ud2XidGKQApdQKIA3Y7O9j6QAygD3+PoggQvRyhujlDNHLGaKXM0QvZ4heviNaOUP0cobo5QzRyxmilzNEL2eIXm1jEJCvtR7feIUYpEIMpdQyrfUkfx9HsCB6OUP0cobo5QzRyxmilzNEL98RrZwhejlD9HKG6OUM0csZopczRK+OR3JICYIgCIIgCIIgCIIgCF2KGKQEQRAEQRAEQRAEQRCELkUMUqHHY/4+gCBD9HKG6OUM0csZopczRC9niF6+I1o5Q/RyhujlDNHLGaKXM0QvZ4heHYzkkBIEQRAEQRAEQRAEQRC6FPGQEgRBEARBEARBEARBELoUMUgJgiAIgiAIgiAIgiAIXYoYpARBEARBEARBEARBEIQuRQxSQoejlIrx9zEEC6KVM0QvZ4hezhC9nCF6OUP0cobo5TuilTOUUsn+PoZgQvTyHdHKGaKXM0JVLzFICR2GUuospdQG4DKllNvfxxPIiFbOEL2cIXo5Q/RyhujlDNHLGaKX74hWzlBKnaGUWgf8TimV4O/jCXREL98RrZwhejkj1PUSg5TQbpRSPZVSfwPuBwYDFwIp/j2qwES0cobo5QzRyxmilzNEL2eIXs4QvXxHtHKGUipZKXULcB/QHzgRGOjfowpcRC/fEa2cIXo5I1z0EoOU0GaUUsp6mQ5cDHwAnA9MA47y13EFIqKVM0QvZ4hezhC9nCF6OUP0cobo5TuilTOUUvZzTgpwKvAFcAIwBDhBKRXtr2MLREQv3xGtnCF6OSPc9FJaa38fgxBEKKV6AAla6+2Nls/UWn9jvf4ASABO1lof8MNhBgSilTNEL2eIXs4QvZwhejlD9HKG6OU7opUzlFKpQJrWel2j5eO01iut189gjHinaq3Xd/1RBg6il++IVs4QvZwRznqJh5TgE0qp/kqp14F1wAdKqUeUUiOtdS77psjiPmCm1cIO0coZopczRC9niF7OEL2cIXo5Q/TyHdHKGUqpTKXUq8BG4B2l1LtKqXo9tNYrvTzM7sB4GhyllIrww+H6HdHLd0QrZ4hezhC9xENK8BGl1H+BAcCfgKnAaUAMMFdrvbfRtpHAAqAIOFdrXdbFh+tXRCtniF7OEL2cIXo5Q/RyhujlDNHLd0QrZyilHgamAL/GPLCdCYwAfqy1Xuq1nUtr7VFKvQtkAGdrrbf645j9iejlO6KVM0QvZ4hegNZamrQWGzAJOIj5YdjLBgD5wBNAL2uZy2v9mUAtMMffxy9aBW4TvUQv0StwmugleolegdFEK8d6DQH2A1d5LYsENgPvAUNsvWiYjJ8MeIBLALe/+yB6BWYTrUQv0avzm4TsCb4QBWjAjl+N1MYi+0dMorWjAbTWHq/PfIBxM79aKRVlfU4R+ohWzhC9nCF6OUP0cobo5QzRyxmil++IVs6ow+TQWgqglIrxwfXaAAAdaElEQVTRWtcAtwDDgHPA6KW11koppY3nwTfAZUBP63Oil+jVGNHKGaKXM0QvJIdU2OPjCZwC7KQhN0EdgNb6YWAPcKJSqpe1P5e1rgKT0+A0YLxSKgOYqpTq3rE96DpEK2eIXs4QvZwhejlD9HKG6OUM0ct3RCtn+KhXMrABOMZ6XwOgtX4ZY6Q7Qik10Nqfi4bnnz8BPwJmKaV6A/NsXYMV0ct3RCtniF7OEL18RwxSYYxSyq0t379m1ts/pEVAHDBKKRWtTfxqpLXuWWAuJta18WzdO5gbpzswboevYfIgBB2ilTNEL2eIXs4QvZwhejlD9HKG6OU7opUzlFIRLenlxRagGBitlErQWtd56fUq0BeYBfWeBraB7zNgDcYb4X3gXeCoDu5GlyF6+Y5o5QzRyxmilzPEIBWGKKX6KKUeAV5QSv1CKTXMWn7I+WC5Brq01vnAV8ApwEBrtX0D9G8gCRjqtX+XUioLOB8zyzcXyAGO1Vp/1End6hREK2eIXs4QvZwhejlD9HKG6OUM0ct3RCtnKKV6K6WeAN5XSv1ZWRWnvAx23tu6tNYHMCEsE2gwwNl6vYQJgRyqlHLb+1FKZSmlrsGEvIzB5GyZorX+b2f2rTMQvXxHtHKG6OUM0auN6ABIZCWt6xowHcgFvgCeBPYBm4D+TWyrgCjr9SjMD+QXQIS1zF73FfCc1+eigYcwbodPYiXfDLYmWoleolfgNNFL9BK9AqeJXqJVJ+o1CvOA9T/gHkw4S4Glo2pBr8HADkzYYrSti/X3DeAjr89EAr8FqoGnRa/w0Eu0Er1Er8Bsfj8AaV34ZZsT+THMjUy6tWwEJmnmF8Aga1kEXln7abgRegNYBhzhta4b8DXwiLV/uwLAJKCbv/ssWolegdhEL9FL9AqcJnqJXqJV4DTgdmA1kGW9TwI+B74HZjejV6T19x7gB+A8r3VuTOjLh3g9FGKqW8X6u7+il2gVqE30Er26TDt/H4C0LvqiG25YfgDuaLRuAlCFiUON8FqeirG+vm29Hw0sxuQ2SMXkN5iGmfk7zdrG1dl9Ea0Cq4leopfoFThN9BK9RK/AaKJVm/X6GnjKu2/AIGAr8DiW54C1PA14AlgAZABZwJsYb4OhQHdMSMsW4ArRKzz1Eq1EL9ErsJvkkApBlFIXKaVOt+NNoT4vQSrGVTzda1uX1no55kdwKjDRWv6Ite144CVlSg6vBn4HxAPrMQkzP8TM3n1j/R/v5JoBj2jlDNHLGaKXM0QvZ4hezhC9nCF6+Y5o5QxLr+uUUon2MkuvGGA7kG0t81h6bcYkaJ8GHGvt4w6MXhOBR4FCrXUO8BvMQ91ijFYLMA+En9r77JJOdiCil++IVs4QvZwhenUS/raISeu4BszAxK16MO6BGU1s8xbmJO9nvbddBScD+4GfWe9/D5xAEy6BmBur0zGxrif5u9+ilegVaE30Er1Er8BpopfoJVoFRsOEFX5k6bUdmNjENvcCS4CxjfQajqkqdZ/1/krguGb0SgLmAX8GTvR3v0Uv0SrQmuglegVS8/sBSOugLxJ6YSyoa4G7Ma7gF3qtt/MRXAKUeJ/kNLgaLsTMvoVUXKpoJXqJXoHbRC/RS/QKnCZ6iVadqFd34HlM0t/fAweBW2lI3mvrdTLGS+C6JvbxMsYzLLOV/6U68thFr8BuopXoJXoFd/P7AUjrwC8T7gLmWq/fBL4FulvvvZOh5WNiWXtY7+0s/9cBReHwYxCtRC/RK3Ca6CV6iV6B00Qv0aoT9foVcKT1+lFMtcERTei1BHibhuTA9oPfGZiHwXR/90X0CqwmWoleolfwNr8fgLQO+BKbSHIGHI1xK/yx1zLbdfDXwF7g7EafOQsT0zrI330SrQKjiV6il+gVOE30Er1Er8BoopVjvQ4zuGEqRXmAn9HgYWDrdTkmme/NjT4zGSgFJvi7T6JXYDTRSvQSvYK/SVLzIEEpNV4p1c96fcj3pr2SnHmt+wKT7f9apVSCtazO+vtPYBtwg1JqoNeujgdygN1KKdXhnQgARKvDsRLxHXZegejlFNHLGaLX4chY33GIXocj433HIFo5Q1tPZADK4NJab8R4ElwG9LdW23o9i0nse5VSarLXrs7AhMTkBrteSqlpSqnB1uvGY73o5SOi1eEopeKsv02N86KXA0SvLsLfFjFpLTfgHGAlxhJ7rcPPngbUAkd4LbMtuUdjYll3A38D7gB2Ajf4u8/t0GoEbczDEG5aWf06G5OY7y7Ry6c+H2n1rZf13ucQizDVax4mnn6wnF8+9VnGemd9lvHeWZ9lvPe9vzLWO9PrGOBCTMUon7SytwMmWGPe5TSUSbf1Gg28i0n8/gRwJ7AL+KO/+9xOvc4Fllv9vkP0arHf47EKBci55VPfzwIKgKcdfCac9ToWYyjy2UM1nPXq1O/C3wcgrZkvBq61Tt79mERqNViu3k0Nys0si8U84LwMdGtifQrwMDAfWAFc4u9+t1GrY4EN1sAw3Yftw1Yrqy8/sc6tA5iKPUNEr+b7jXmI2GRd5HOtv2fY/aZR6Ea46uXVl0sw7sq7MB4B1cAVXnqpRtuHu14y1jvTS8Z7Z3rJeO/jOSJjvWPdLsQk+d0DrLN+k3+kIU9W47G+Sf0sLb7EqkLYaJso4DbMw90i4DJ/97sdev3Ua6x/CCinUViP6FXfj6OA1dY5dYEP24etVlZfvMf5+7FyG4leh/XB7udsa8wqxNyvVgE3eI1djfUJS7269Lvx9wFIa/SFwDCgGMizLuxZQB+MS/c/rG1cXtu7m9iH9/rLgUqMJdeFqRQQ02j7RH/3u41aKcys7w/AUku3e7HieJvYPmy1so49CpNsz4OZmc3ESqwqejWrQQQmDONlTMz4RIw77h7gF96ahLte1vn1b8wD3d+AkcAo4EnMjdKRcn4dcuwy1jvTS8Z7Z3rJeO9MLxnrfdfKbY3x6zAz/yMwYSu3Y0JSLvXl3PLS8yjrPD3D2ncKENdo+6CtQghkYKos5gF/AAZav8/vgGcbayR6cQzGWPstJr/aK0BPa11jI2e4axUJvG718W6gN1YS7Wa2D2u9vPrwLibkbiiQDfzdOtf+aWsienXxd+LvA5CmAaJpcPPrBZwK9PZanwqswTzYHZZI09rmWJpw+wbiMDNYz2Nm4t8FrvB3n9uhVSLmIdceDH6HuYmcCNyEmR1obWYgLLRqpJd9ft2H8S44voXPNL7gh51eXu9PxNxIHu+1LBkzI7IHq3wrhz6MhI1ejc8ZTDnc3wDdvJYlYWLrj2jms2Gnl9W/njLWOz6/fivjvU96ua2/Mt77rpmM9b7pZN97/cL6PSZ6/T67YR7qLm5B4780s24F8DFwo/Ubv9Xffe0gvWxtjgf6eC1PAD7CeFg0N9aHnV5W364APsDcu56L8RpudgwLV628fos3YYzDPo8x4aiXV/+mYIzDl3kt64aZgPDg9QwgenXh9+LvAwjnhplResI6gc+1ljUZzgIsBF61XnvfEF2KueE8CPwJr9lijOV8EMYK7LHaa94XxWBpGAv2U1Yf3vNaHuP1uhemtPLfaNqqHRZataJXP4y3yp+s9xcDL2AMe409WEQvM/tRC6RY7+1S3b+wtr0vTPXqZ2lwGjDUa/lhM0HADIzr/cAwPr8O04vmXcDDeqy3+tMXOB0ze+mtgYz3zvSS8d53rWSsb1qv/hij2o9sbbz1abTtIMxYP7HR8sssvcqBf+HlwQHEYDyInvXS63Ug29997yi9aCb3EcZQ+SWQEI56YTyCh1uvI2i4BkY12m47xrjbvbGe4aJVK3r1wtw3PG8tv9gaf/4BnBmO51Yreo2x+jbRXmf9Pcda/nw46uXv5vcDCLfm9YO4CpOfYD6mHPB5rXzuCWBdo2XZmASZ99MoIRvGpfAMTPhHLXAXlstrMDXMTPiH1g/9W2AVJlzDngG29bTf34GZzRwQblr5opfXuZSPSayaB3xmvfcA12NuqDOtC1xY6wWMs36ndshGjPV3svWZCswsenqY6JUKPAOUYcINCjClzEc2s/2PgfWYh5Q/YEJhVBidX470sj4TlmO91ZcU4D+YfA7rMR5Qj2IZOpHx3pFe1jZPIuO9L+fWOEw+kbAf6zFjdAxwj6XXSqAIkwelyYctjLfnWqvvDwAzaXjQ+x/mYa7xpITCGAK3Ybxo7wLS/N3/ztaLBs+W24DdjdZFAZ+GuF69rXPEAyxsYbto6+/1mGvonHDTqjW9aLgm/hFzLSzAjPXvAVtpCOWz8+CF9G/Rl/ML6GH18w7rvT1ODaXBsDQgXPQKlOb3AwjHhnlIWQHcgskt0GxeB6/PPIF5qOvvtcwFxLfwmfOxwmf83ec2aNQNEzdeB7wFzLOWP495sOvVzOcGAKUYF1bv2c+Q1cpXvbwuXCOsC9WfMLN5iZibqVeAjcCJ1nZJYayXXdUlDTP7sRuvnB+Wdt9hbr7/L9T18urHrzA32z+2zqmZmGpBb1rr7XPMhUkQWQL817qgb8e4ldsJu5NFryaTIofVWN+oHzdgjAWnYm4Ob8YYUZ63f18cOjseluO9j3rZ3hmjwn28b0Wr/1p6JFq6yViv6ydrtgEXYe5Tj7HGsm+BsdY29nh/GsbA9zzmwfh7a7y39Upo4f8cCfw8nPTy+szvMBMUoxrp2WzOmWDXC/P887p1fnyLMaL8yFp3mKertdyFMbQ8aJ9LNBj1QlYrX/Ty0mEA8BUmp1sWVh4jTJ7F7cDV1vuQ/i22opdteIrB5Lsr49AUHT+1PrMbqxptqOsVSM3vBxCODeMdtYFD3Z/jmtnWngn+tfXDanW2rfFFL1gbcB4mKWiE16D7f5hZzJaSsz6CSaycGS5a+aqX1w3PGTQKIQCGY6pN3NecLmGkl7db7lQajCm3YQwG+zEPHe/TQhn1ENMrAZPf6IVG59KNwM7GfaYhSbetbR9MeMInNH/jGXZ6eW0ftmO93RdgGfByo2XXYrxNrqaRZ6z1OlzHe1/0ssf9s8J5vG9FqzoaHtYmAjvDfay3+nOfpUOU17LhGCPegxwaPpuC8Ryzx/pk4EXrdxnh774Ekl7WeWfrdBnGoD7O38ffhTolYrx3bsRUOvsWeN3Wpontba3+iDFKTfN3HwJVL0yessxGy7KBBZgQviZzlYVS81Uva7xaaY33/8J4spdb4/7jwBuhNqYHenMhdBpKqROUUuOUUknW+whrVQLmIl2olBqhlHoH+Eop9ahS6gjvfWit66yXGzAz7InWvlRz/1dbv7ZgorFWAFrrF7XWy7XWtVprj7X4ICbEYLT1uaZ0eAiTO+N4r/1HNrV9MGoFbdcLczOE1vp1rfUua19KKeXWWq/DlLrOthc3/r9hpNcor+0WA6dgLmxXA4OBi7TWd2IKErit/3HYeBpKemHGniJMSIJ338ZjvAxsXNb6HK31Lltb63xbjrkRyGjq/4ajXvZ5Ey5jPTSrVzrm3MlptPl7mATRl9IwjnkTluM9vuk1CkBr/Wq4jPdt0GoTcLlSaqzW+jvCb6w/Ryl1slJqQKNVpZjcWNXWdhHWOfMcMBc4zlru0loXaq33eo31BzAVHqMxRpmQob16YR5y7fuNFZgE+DHWZ0LqmawZrcqAM7TW/9Raf4UJIZ6llBqntdaNNfDS6h4gFjjZa3yPtv6GhG7t1Utr/aHWOtfal7J+m9swHj89Mc+eIUMb9YoA0FrvBc7E/D6PxeQWPF9rfRtmkrq8qfNR6DxE6A7EvqFTSl2qlMrHJEn+EPhaKTVRa11rbRoL7FZKXYhxcVaYxOYzgDeUUkc33icmVroI64Y8WG9+bFrTqont7XO1AHPeJkDTOmitV2NcNq9VSo1RSp2GmYkKWt06UC+P1zb2wKy11nVKqUSMu6tLa+0JVq2g4/Sy0VqvxHg2Zmmt52it31NKJWP02mdt4yFI8UUv64F2FXCkUuoupdRUpdRjmATBo5VSv1NKRduGFaWUu4l/lY15qMvr/F51Hh2sl8d7n4TYWA8+67UHqAQGKaX6Wss0JndPNSbUeIa93N5nuI73Puo1zWufkfY2oTTed5BWwzAPLUprvRz4CaE/1h+jlMrFePbcAyxRSl3ktWk5cFApNdX+qPX3cYxX2fHWA6+nmbF+jPV3V4d3ogvpJL3sdYmYc2oSBPd5Bb5pZY01VfZ4hKlOtgcT+tTcft1a61LgMeBCYI41zt9s77Oz+tSZdIZeje7rPdb7ftay4k7tUCfTQXrVnyta681a61swOT2P1Fq/rYyRcygmBDloz61gRAxSHYh1k9wfExf+BDCPhpKlTyulTrA23YRJ7ns15sHsUq31jZiZ8y3Ar5VS2da29ne0HDOTUtMVfelsfNDqGGh4qG00oxSL8WJpcmbEWvYqMBZTOvZ1YJxSKq4z+9SZdIZetoFUKRWhlOqBuQlPxlTmCGo6SS/7Qhdj6XUNZiblha7pVefhYOy6HfgnDYl9J2LCWbYDvwReUErZNz+2YSpCKdVdKXUlZty7xZ5FDlY6WK/+1rb2Q11IjfXgk14nWps+icnLcJLXxxMxxoRaYJ51w1hvbArT8b4tetVA6I33HajVXIxHD1rruhAf62MwD2ifYIyWR2J+O3cqpa6xNl2JGYdmWp+rsQx2GzDhj+OAkdY6e6yPtMb6SzBGlt9rrYu6rncdT2foRYOxahPWORcK+KDVVVA/ZtsT9MswXizHKqWyLSNKYy8p23v4RYwny8uYcX6UZVgPSjpDL6/7+kilVHfMc2Y2phptUNOJ51eNUirW0uunmKTnD3VJp4QGdADEDYZSwyQq3wUM81o2E+NCv8hr2TaMpfZC672dbO0YzIzd0Y32O8Da/kZ/97GLtPq2mc9MwIQZ/M563ziGOgVTkWOTpdeTeJWlD+bW0XphEm/+CvPAvIiG+OmYxroGY+uk8+sUTC6DRZgZlGsxRuNQ16txZZffYPJB9fZadgLm4e48630aJo7/75ZeRcCfCZEEkB2tl9fykBvrfdDL+9r4DqYK3MPAb63X/wZ+AeQ12me4jveO9cLcZIfkeN9J51bIjPWNjxnjEVaECVGxl/XBePMU0pA4eiHwLsZbDExIGsARmPD2qdb7ROs7uN/6zAFMcuVmEwIHcutsvez/gZn4qqSF3GSB3hxqVYBXQQD7s8DRmIID91rvXY32mWT9/tZjvM2eAIb4u++BqBdmQvVWa4xbaJ13dxKk911ddH4djRnrl2BCb2/GK7estK5p4iHVRpRSo5QJwxhkvY+yVkUCVVrr9aohN8g3mIFhilLqHGu7e6y/062/2tr2E8wPItvar513qg5z4/1k5/Wqc2ijVtOUUqdb20d47S4XK5eD5f7cOMygL8aldwGQrrW+QpsZqqChC/WKxVzoj7T2MUBrfZvWurIJXQOWrtDLdhXGXPDGW/sYorV+WAdZuEsb9ZrqpVc8MAdYqbXebe9Xa/0BRp8h1qLuGIPBHExOlmFa6z9orSs6vZMdSBfoNdjazvaQCtqxHtp1bTzb2u7nmDLVszCeKY9qrX+KCX88oKyQK4twHe+d6NXP2i6GIB/vu/jcCoWxfrxS6kHgD0qps5VS6daqPph7zvpwOm1CjB/CGHZ/ay1+AhN6d7L13vZUWWi9zrTeR2Jy5Y0DvsHo9RttwqyChq7Sy+veIhGTCiDovFfaqJXGFI5pzCJgPnC6UipJGy+WKK/7rlTgKIyOGVrrK7XWGzulY51EF+hle9rVYkKRJ2Puu7Ks32Kw3Xd1xfnlnarDLrozUGv9D31oblmhK/C3RSzYGmZg/CvG6uwB/mQt965eVgeMt97HWn9/Zm2/1HrvwpT2XgOM8Nr/LExVl0v83dcA0Gpxo/3Z1TbWAP+2Xh9WrYsgreriD70wDyiil7Pzq8kKcYHeOkovzMzuZkx1s0RbO8zDSLG9X2t5crifXz7odZu/+xogei1ptL+0Ru8fwPJ2sfdpvQ7X88tnvbyWBeV438Xnlre3QbCO9f0xyXoPAB9hHrQOAh/Y+mA85K707jPGsHQnJpVEtDV2vY/xHBjjtf9jMJ5BP/ZaFit6tarXyf7uawBotReI99qfvf5EYKv1Ox+KCcEd7LVduJ5bvup1CzDIWhdDEI7zfj6/xBvK39+9vw8g2BomK38J8B9MDOsivMopYxKJLgM+bPS5lzHW/b3APGvZWZgysV9bF6zxmNKTS7EeXIK5dZBWs61l9qCSCLwJLPB3/0Qv0SuYWgePXXdY72/EJMwcg/HoWUmjssPB2kQvv+g1y1pm/x5dmJvrY4HvgKv93U/RS7QK5IYJNbkPc195AhCPMZT8BOM5YaeJ+AD42nrtbeCdjHkAtLc7CuNpsRI4FVNs4WnMg2Ksv/sregWlVuda791e6wZiDBAeTKW0MmCcv/sseole0rro+/f3AQRbw+SYud56fTYmLvUmr/Vua3ktJpHaDcDbmHLDvwAW42XZxSR+XItJcltk7e8oa33Q5SroBK0ub2K/CzEzUCn+7qPoJXoFS+vgsSsOUyG0BvjeGrtW2GNXKDTRyy96Xe61fSYmdPE/mNnSd4Ce/u6n6CVaBXrDeGNe3GhZP0xo67PW+4swD2cTrfe2oS4d+BR4y3qvMHntFmJynxZY+tvG9qC+TxW9/KLVG40+f4z1e/Vginqc4+9+il6il7Qu/u79fQDB3DBJe9/HzHwkN1p3LvAexk33Q2C0tTwPOMN6bScyj8fMokzxd58CWCvlNeicA8zxd59Er8BpolfX6mW9T8DkwLtC9BK9OkGvXsCfrP0c7+8+iV6B0UQrnzSK8Hrt7UGwGviX9Xogxhv/vSa2ewt4CeNBZl8X7XLok/zdP9ErJLSK9lp2M7ADONbf/RO9RC9pfvru/X0AwdqwXAGB64ANWO7ejX5M3Tg0H8EwTNLksPpRiFail+gVOE30Er2CRS+CPMRF9BKtAqFhJlwSMdUo/2hrifEq8+BVscpatwJ4zt/HLXoFfmuPVoRh3h7RS/SS1nSTKnttx86+/xawEzhDKRWtta61K0NorSu0Vf1AKdUbuAmTM+pz/xyy3xCtnCF6OUP0cobo5QzRyxkdppfW+mBXHrifEL18R7RqA1prjakilYUJT0RrXae1fgUTtni7UuoOpdQIpdTVGK/9Z/11vP5G9PKd9milw7CKmejlDNErfBCDVBuxfiRoU27yU8yP5UzvdUopt1LqcuA3mLwGxwL3ed88hQOilTNEL2eIXs4QvZwhejlD9HKG6OU7olW7OAdjxNvkVe4cjLfZ05hQ4jeBezDJ4L/q8iMMLEQv3xGtnCF6OUP0CgPEINUOvH4YbwH7sG6MlFJxSqlkrXWdtX4W5kcyUGv9PjTcPIULopUzRC9niF7OEL2cIXo5Q/RyhujlO6KVM7z0OhZTmarM9hxQSsVqrUu11n/GFNi5Guihtb5Va13tp0P2K6KX74hWzhC9nCF6hRcqDK/PnYJS6i+YG6PngFFAlNb6LMudvMq/RxdYiFbOEL2cIXo5Q/RyhujlDNHLGaKX74hWvqGU6gds/P/27hg1iygKw/B3wS64jizB0s5CBEs7l+AKbAJxFS5DEIQEbSxFcBGp3UA4FpMQhBDuIXD/n8zzrODyVjOHuWeSvK2qb2OM0yQfsu1meb/HQd1D9JqnVY9ePXrthy+kHunm8/CTbH9xOU3yKcmLJF+SxEPRHa169OrRq0evHr169OrRa55WbW+SXCXJGONzkl9JXie59EJ3L73madWjV49eO/Hs0Ad4Ap5nu7v6MttviM+q6vthj3S0tOrRq0evHr169OrRq0eveVpNutmb9Srbvq2vSf4keXd7jZH/6TVPqx69evTaFwOpR6qqv2OMiyQfq+rnoc9zzLTq0atHrx69evTq0atHr3lazauqGmP8SHKd5Lyqfh/4SEdNr3la9ejVo9e+2CEFAABP0BhjuN4yT695WvXo1aPXfhhIAQAAALCUpeYAAAAALGUgBQAAAMBSBlIAAAAALGUgBQAAAMBSBlIAAAAALGUgBQAAAMBSBlIAAAAALGUgBQAAAMBS/wDfgQTgkQrUWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['axes.linewidth'] = 1.5\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(final_prob_df.reset_index().index.values[:-7], final_prob_df['REC_INDEX'][:-7], label=\"Actual (1967 Q4 to 2019 Q3)\")\n",
    "plt.plot(final_prob_df.reset_index().index.values[-8:], final_prob_df['REC_INDEX'][-8:], label=\"Predicted (2019 Q4 to 2021 Q2)\")\n",
    "\n",
    "plt.xticks(np.arange(0, 215, 16), final_prob_df.index.date[0::16], rotation=25, fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "\n",
    "plt.ylabel(\"Probability of Recession\", fontsize=20)\n",
    "\n",
    "plt.legend(loc='upper left', fontsize=15)\n",
    "plt.title(\"Recession Probabilities, 1967 Q4 to 2021 Q2:\", fontsize=25)\n",
    "\n",
    "plt.savefig('final_recession_probabilities.png', dpi=80, bbox_inches='tight', pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
